Reviewerly/exHarmony huggingface dataset:
Authorship Attribution in Greek Literature Using Word Adjacencies

Authorship attribution stems from the idea that one can use a text to derive useful conclusions about its author. It is a rather old idea, that has recently gained momentum due to the major technological leaps achieved in computer science. These leaps enabled researchers to process large amounts of information in reasonable time and to employ sophisticated algorithms for extracting textual features that may indicate something useful about the author. In this paper, a method for authorship attribution is investigated that resorts to Word Adjacency Networks (WANs). The method was originally proposed for author attribution in English corpora by Segarra, Eisen, and Ribeiro [23]. The paper builds upon this method by demonstrating its capability to capture the stylometric patterns of authors in Greek literature. Contrary to English, Greek is a strongly synthetic language. The data used in the experiments comprise literature pieces from 20 Greek authors that belong to two different groups. The first 9 (i.e., Eftaliotis, Karkavitsas, Kondylakes, Moraitides, Nirvanas, Papadiamantes, Rhoides, Vikelas, and Viziinos) form the first group. The remaining 11 (i.e., Delta, Empirikos, Karagatsis, Kastanakis, Kontoglou, Mirivilis, Politis, Prevelakis, Terzakis, Theotokas, and Venezis) form the second group whose nucleus is the so-called Generation of 30s’. The problem is formulated mathematically and the attribution algorithm is tested for a wide range of parameter values. The experimental findings show that authorship attribution with WANs can give satisfactory results as long as there are adequate training data. After fine tuning the parameters of the method, the total attribution accuracy was found to be in the first group of authors and in the second group of authors. This demonstrates the potential of the method and its applicability to morphologically and syntactically rich languages.
An Improved Flower Pollination Algorithm Based on Logistic Chaotic Mapping and Natural Mutation

Function optimization has always been a hotspot in the field of computing. It plays a crucial part in engineering application today. In recent years, many swarm intelligence optimization algorithms have been proposed and applied to solve function optimization problems. The traditional swarm intelligence optimization algorithm, taking flower pollination algorithm (FPA) as an example, has the problems of low accuracy, slow later-iteration convergence rate, and easy to fall into local optimal solutions. To solve these problems, a new flower pollination algorithm based on logistic chaotic mapping and natural mutation (LNFPA) is proposed in this paper. LNFPA uses logistic chaotic mapping to generate the initial population, cross operator to perform local search, and natural mutation operation to boost the search after each iteration. A large number of benchmark function tests have demonstrated that LNFPA has a more stable performance, faster iteration speed, and higher accuracy than FPA. In addition, this paper also analyses some shortcomings of LNFPA, and possible improvement ways in the future.
Fractional and Integer Order Chaotic System-based Pseudo Random Bit Generator for Secure Image Encryption

This paper presents an implementation of continuous-time chaotic system-based pseudo random bit generation by using fractional order Chen-Lee and integer order Lorenz systems. Since chaotic systems are very popular in random bit generator applications, both fractional and integer order chaotic systems are used in the proposed design. The electronic circuit equivalence of chaotic systems are designed and simulated in Orcad-Pspice environment to use as an entropy source to the proposed a new post-processor algorithm. The generated random bit stream is tested by using NIST 800.22 statistical test suite to demonstrate the randomness of the proposed system statistically. Also, an image encryption application is executed using a pseudo random bit stream. The results of the statistical tests and image encryption show that the proposed pseudo random bit generator is suitable and can be preferred in secure image/media transmission/communication systems.
Chaotic System-based Pseudo Random Bit Generator and Post-processor Design for Image Encryption

This paper presents an implementation of both continuous and discrete time chaotic system-based pseudo random bit generation by using three dimensional Lorenz system and one dimensional Logistic map. As chaotic systems are preferred in random number generator applications, two different chaotic systems are used as entropy source in the proposed design to make the design more secure. The electronic circuit equivalences of chaotic systems are designed and simulated in Orcad-Pspice environment to obtain values of state variables in fractional number format and input to proposed post-processor algorithm. The generated random bit stream is tested by using NIST 800.22 statistical test suite to demonstrate the randomness of proposed system statistically. Also, an image encryption application is executed using pseudo random bit stream. The results of statistical tests and image encryption show that the proposed pseudo random bit generator design is suitable and can be preferred in secure image or media transmission systems.
Comparing Student-Based Context Priming in Immersive and Desktop Virtual Reality Environments to Increase Academic Performance

Research suggests that 3D virtual environments can be designed to prime engagement, creativity, and improve performance on many cognitive tasks. In this paper, we report on a study that compares the efficacy of context (environmental setting) on the priming of these desired effects within Desktop Virtual Reality (DVR) environments compared to Immersive VR (IVR), viewed from within a VR Head Mounted Device (HMD). We presented a 27-minute seminar “The Creative Process of Making an Animated Movie” to 68 participants within 4 different learning spaces: two with IVR (Prime and No Prime) and two with DVR (Prime and No Prime). The priming scenarios for both IVR and DVR environments included subject matter and popular culture visual artifacts related to animated movies and characters placed within a theatre classroom. This was intended to create a situated learning effect. The No Prime condition was presented in a standard classroom theatre without visual artifacts or any subject matter augmentation. A 20-question multiple-choice content test and UX survey were administered following the seminar while an affective questionnaire measuring anxiety and positive affect were provided before and after the seminar. Increased academic performance was observed with a significant difference in both DVR and IVR priming scenarios compared to the no priming conditions.
AgileGCN: Accelerating Deep GCN with Residual Connections using Structured Pruning

Deep Graph Convolutional Networks (GCNs) with multiple layers have been used for applications such as point cloud classification and semantic segmentation and achieved state-of-the-art results. However, they are computationally expensive and have a high run-time latency. In this paper, we propose AgileGCN, a novel framework to compress and accelerate deep GCN models with residual connections using structured pruning. Specifically, in each residual structure of a deep GCN, channel sampling and padding are applied to the input and output channels of a convolutional layer, respectively, to significantly reduce its floating point operations (FLOPs) and number of parameters. Experimental results on two benchmark point cloud datasets demonstrate that AgileGCN achieves significant FLOPs and parameters reduction while maintaining the performance of the unpruned models for both point cloud classification and segmentation.
Flood Adventures: A Flood Preparedness Simulation Game

Knowledge of how to prepare for flooding remains an important educational need as illustrated by the devastation caused by recent flooding events across the USA and in other global locations. To address this need, we are developing Flood Adventures, a two-stage VR learning game with best practices for flood preparation focusing on how individuals can prepare for flooding and communities can mitigate flood risk. We present our VR game learning model that guides our design and development work. Our prototype development work on the first game stage that focuses on flood preparedness is presented. We conducted usability testing with the initial prototype version of the game with twenty-four adults. Findings from our usability testing found that players used spam clicking strategies during game play. Recommendations to enhance the game are presented.
Machine learning methods for anomaly detection in computer networks

With a large increase in the amount of data that are transferred via publicly available computer networks, the global demand for new protection and prevention methods could be observed in recent studies of many research groups. The paper deals with anomaly detection, focusing on cybersecurity applications, as there are only few papers that address this topic. Four methods, such as DBSCAN, One-class SVM, LSTM and Isolation forest were used to solve this problem. During the experimental part, the implementation and experiments were performed to examine the performance on common dataset to assess the ability and further possible applications.
Work-in-Progress—Using Virtual Reality in Museums to Assist Historical Learning

Museums are always looking for new ways to attract and engage their visitors. Virtual reality (VR) is becoming an increasingly viable option for doing this and enables the recreation of historical sites/structures for visitors to explore. This paper describes a VR application being developed in partnership with the Bawdsey Radar Museum to recreate the radar transmitter towers that stood on the site and were pivotal in winning the Battle of Britain. This paper describes the application’s development and discusses user trial results to determine whether VR can be effectively used within a museum setting to assist in historical learning.
INTERPRETABLE LEARNING-BASED MULTI-MODAL HASHING ANALYSIS FOR MULTI-VIEW FEATURE REPRESENTATION LEARNING

In this work, an interpretable learning-based multi-modal hashing analysis (ILMMHA) model is proposed with appli-cation to multi-view feature representation learning. In the proposed model, a cascade network structure is first utilized to reveal the intrinsically semantic representation of input variables. Then, a multi-modal hashing (MMH) method is integrated with the explored semantic representation, gener-ating an interpretable learning-based model for multi-view feature representation. Since MMH is capable of measuring semantic similarity across multiple variables jointly, it provides a natural link between the explored intrinsically semantic representation and its similarity across multi-modal data/information. Benefiting from integration of the cascade structure and MMH, the ILMMHA model leads to a new multi-view feature representation of high quality. To demonstrate the effectiveness and generic nature of the ILMMHA model, we conduct experiments on the cross-modal based audio-visual emotion and text-image recognition tasks, respectively. Experimental results demonstrate the superiority of the proposed model on multi-view feature representation learning.
Work-in-Progress—eXtended Reality Training for Safety and Medical Procedures: Experiences From a User-Centered-Design Approach to Implementation

eXtended Reality (XR) environments appear to be a promising approach to training in various contexts; however, their potential for safety and medical procedures is still not fully understood. The aim of the presented research project is the user-centered implementation of an XR training for various occupational groups and volunteers of first responder organizations (e.g., physicians, paramedics, health care workers, police officers, firefighters). To achieve this aim, the project has followed three implementation steps: First, multi-stakeholder workshops were conducted to define two use cases for the most promising application areas. Second, based on the previous step, identified use cases were implemented as XR training scenarios. Third, scientific evaluations of the training applications are conducted focusing on subjective and objective performance indicators, learning success, training experience, acceptance, and potential barriers from an end-user perspective. As the last step is still ongoing, this paper puts emphasis on the outcomes of the stakeholder workshops, the respective decisions taken towards selecting two use cases, and the design of the evaluation study.
Doctoral Colloquium—The Potential of Mixed-Reality Technology for Motivating Dentistry Students in Higher Education

New technologies are becoming an essential part of education. Advances in these technologies are changing traditional learning processes, thus affecting students’ motivation and strongly influencing their learning outcomes. Mixed reality (MR) is one of the emerging technologies that may affect students’ learning motivation, with the potential to improve their performance and enhance learning outcomes. Nevertheless, it remains unclear how MR affects students’ motivation to learn. Thus, the aim of this doctoral colloquium is to propose how research may be conducted to evaluate the efficacy and potential of MR technology for motivating dentistry students in higher education. In this research, we design and develop a gamified educational MR application by combining the (Analysis, Design, Development, Implementation, and Evaluation) ADDIE instructional design framework with the (Attention, Relevance, Confidence, and Satisfaction) ARCS model for motivational design that is integrated with gamification components, called the ARCS+G model. The MR application is to be used by dentistry students at Umm Al-Qura University in a fundamental implantology course to train them on pre-operative planning using dental cone-beam computed tomography (CBCT). In this setting, we will investigate students’ pretest and posttest learning motivation with the Instructional Materials Motivation Survey (IMMS).
Which Types of Learners Are Suitable for the Virtual Reality Environment: A fsQCA Approach

While virtual reality has become a research hot spot in the education field, few studies considered the students’ individual differences (e.g., attitude, knowledge), which may influence their learning. This study aimed to investigate, which types of learners are suitable for the virtual reality environment. A fuzzy set analysis was conducted, we found: (1) high autonomy virtual reality environment suitable for students with a solid foundation of knowledge; (2) low autonomy virtual reality environment cause more cognitive load for learners compare to high autonomy virtual reality environment; (3) a positive attitude towards VR may lead to worse learning outcomes for learners with low prior knowledge. The findings indicated that teachers should personalize their use of educational technology based on their students.
Work-in-Progress—Post-COVID: Adapting Education to a Changing Educational Environment through Immersive Technology

In a Post-COVID world, Hybrid classes remain essential as students face challenges in attending them in person. This creates additional engagement issues. The creation of an easy-to-use platform to enable this mixed-mode classroom is critical for teachers that are frustrated by the difficult management of hybrid classrooms. An aspect is student engagement, which today has no established analytical mechanism to evaluate during classroom sessions. This work-in-progress paper describes a technology that has the ability to obtain real-time analytic data to determine the engagement of all students at all times and helps teachers to know which students are lagging early on.
Direct determination of arbitrary dimensional entanglement monotones using statistical correlators and minimal complementary measurements

Abstract Higher dimensional quantum systems (qudits) present a potentially more efficient means, compared to qubits, for implementing various information theoretic tasks. One of the ubiquitous resources in such explorations is entanglement. Entanglement monotones (EMs) are of key importance, particularly for assessing the efficacy of a given entangled state as a resource for information theoretic tasks. Till date, investigations towards determination of EMs have focused on providing their tighter lower bounds. There is yet no general scheme available for direct determination of the EMs. Consequently, an empirical determination of any EM has not yet been achieved for entangled qudit states. The present paper fills this gap, both theoretically as well as experimentally. First, we derive analytical relations between statistical correlation measures i.e. mutual predictability (MP), mutual information (MI) and Pearson correlation coefficient (PCC) and standard EMs i.e. negativity (N) and entanglement of formation (EOF) in arbitrary dimensions. As a proof of concept, we then experimentally measure MP, MI and PCC of two-qutrit pure states and determine their N and EOF using these derived relations. This is a useful addition to the experimenter’s toolkit wherein by using a limited number of measurements (in this case 1 set of measurements), one can directly measure the EMs in a bipartite arbitrary dimensional system. We obtain the value of N for our bipartite qutrit to be 0.907 ± 0.013 and the EOF to be 1.323 ± 0.022. Since the present scheme enables determination of more than one EM by the same limited number of measurements, we argue that it can serve as a unique experimental platform for quantitatively comparing and contrasting the operational implications of EMs as well as showing their non-monotonicity for a given bipartite pure qudit state.
Enhancing K-16 Science Education with Augmented Reality: A Systematic Review of Literature from 2001 to 2020

This study conducted a systematic review of 20 years of literature on the usage of augmented reality (AR) in K-16 science education. A total of 89 articles were selected as the research sample pool of this review after initial literature searching and manual filtering. The research findings were analyzed and synthesized through five interrelated aspects of AR-supported instruction: metadata, research methodologies, instructional contexts, instructional design, and technological features. The results revealed the trend patterns in AR-supported science education in terms of publication, pedagogical assumptions, and technological affordances over time. In addition, we reported the contextual factors in AR pedagogies and instructional functions. Finally, practical implications for teachers and AR program developers are proposed and discussed.
Practical Exploration on the Construction of Theoretical Courses of Composition Technology in the Age of Artificial Intelligence

The theory of composition technology is to study the basic knowledge and skills of musicology, instrumental music, composition, and composition technology, including harmony, polyphony, musical form, orchestration, and so on, and then to analyze, create, and edit music. The current theory of composition technology has resulted in the phenomenon of relatively single form. The current method is the traditional way of composing music through the creation of composers. The defect is that various elements cannot be integrated together, and the meaning of music cannot be perfectly presented. In order to solve these problems, this paper proposes the use of recurrent neural network algorithm and backpropagation algorithm in artificial intelligence algorithm. It aims to study how to innovatively integrate the composition technology theory course with the current network technology. And it utilizes recurrent neural networks in artificial intelligence to help design part of the analysis of musical characteristics, through the evaluation of the music effect generated by automatic composition. The results show that the accuracy of note prediction obtained by the automatic composition method on the basis of objective evaluation is 81.93%, 90.15%, and 92.62%, respectively, on Top1, Top2, and Top3, which basically meet the current basic requirements for composition technology theory.
Blockchain for Secure Data Sharing in Fog-Based Smart City Systems

The proliferation of the Internet of Things (IoT) in various smart city systems results in massive amounts of data that IoT sensors and devices generate. Many smart city applications use the computing power of the cloud for data processing. However, transferring data between IoT devices and the cloud can result in high latency for many applications and pose several security challenges. Fog computing aims to address these issues by analyzing sensitive data on an on-premise fog node rather than on a data center outside of the organization’s control, dramatically improving data privacy. To ensure secure data sharing between city stakeholders, such as government agencies, Blockchain technology is more suited for this task. The Blockchain-based architecture we propose serves as a platform for secure data storage and data sharing in a smart city. Selected smart city fog nodes are used to share data among stakeholders and establish a distributed shared database. Also, in the context of smart healthcare as a use case, a smart data sharing contract enables trusted decentralized data storage, authorized data sharing between healthcare entities, automates data management, and defends against unauthorized second-hand sharing.
A Decentralized Collaborative Strategy for PTZ Camera Network Tracking System using Graph Learning

In this paper, we propose a collaborative strategy for an autonomous PTZ (Pan Tilt Zoom) camera network, for improved target vehicle tracking, in real-world traffic. In addition, we explore ways to improve the target-tracking system to adapt itself to unexpected changes in traffic patterns. In the exploration phase, the camera nodes utilize the target identification information shared among themselves in the network to learn a graph. The vertices represent the camera nodes, while edges represent links to immediate neighbors, and edge weights represent the distance between the nodes in terms of the time taken by the target vehicles. In the exploitation phase, once a target vehicle is identified, the camera broadcasts the information to the neighbors. In turn, the neighbors consult the graph to position themselves better for capturing footage of the target vehicle. We carried out two agent-based simulation experiments to test the strategy. In the first experiment, we compare the proposed strategy, which uses the learned graph, to a baseline where the cameras operate independently for scanning traffic. In the second experiment, we compare the strategy to an improved adaptive version of itself, in which the system learns online continuously by observing live traffic. The results show that our cooperative camera network outperforms the baseline and the adaptive strategy outperforms the static one.
Transparency and Technology: How to Transform to Sustainability by Applying Blockchain Technology

These days no company can be considered sustainable unless their suppliers also are sustainable. So how can a company's sustainable purpose incorporate the actions of other supply chain participants? How can a company assure that its suppliers ascribe to the same goals? How can a company know their suppliers, their suppliers' suppliers, and all supply chain participants' responsibility performance? The answer comes with the emergence of new technology, and in particular, with blockchain. With blockchain and other emerging technologies, traceability at scale is more feasible than ever before, but how can it be designed to incentivize participation from all the important actors in a supply chain? In this chapter, we will explore approaches to using blockchain for transparency along supply chains and how to incentivize participation.