Reviewerly/exHarmony huggingface dataset:
Case study: an international business negotiation course design and implementation of a VR-based experiential learning environment

Experimental learning is one kind of contextual learning, which places learners in a variety of virtual or real contexts to acquire knowledge, gain ability and experience, and generates emotion through experiencing and perceiving, communicating and exploring, and self-reflecting. Applying VR technology to experiential learning involves both technology and teaching. Based on the Learning Pyramid, Constructivist Learning Theory, and the characteristics of virtual technology, this study proposes a theoretical model of virtual reality experiential learning, which takes the international business negotiation scene as an example and employs such software as 3Ds Max and VR-platform to implement an experiential learning context. It is found that, compared with traditional teaching, this learning context has effectively increased the learners’ memorization and understanding ability, their interest and motivation in learning, enhanced their learning experience and improved the learning effects. This study not only provides a theoretical framework for and verifies the promotion effect of virtual technology on learners but also enriches the research examples of virtual reality experiential learning, which has certain theoretical value and practical significance.
Encryption Algorithm Using Polygon Triangulation and Catalan Numbers

The present COVID pandemic has transformed a physical world to a digital world. Electronic communication has become a major part of the human life that leads to a threat to digital network. So, hiding and protecting the information against unintended persons are highly essential nowadays. This can be done by encryption process. Encryption techniques are derived from mathematical concepts like number theory, graph theory, and algebra. The present paper explains a symmetric packet cipher using polygon triangulation and Catalan number of applied number theory. Here, a natural number n is secret between the users. The Catalan number Cn and number of triangles of n-angle Tn have major role in encryption process with simple logical XOR operation. To protect the cipher against different active and passive attacks, to achieve avalanche effect, the present plaintext packet is concatenated with the previous cipher text packet.
Compact Public-Key Encryption Using Learning with Rounding

Data gathering from IoT devices could be the target of cyberattack in the Internet of things (IoT) systems that utilize data from the real world. Because of this, encryption-based defenses are becoming increasingly important. Compact public-key encryption (PKE) scheme provides encryption with minimum key size and low-computational complexity. Its goal is to broaden cryptography’s uses on constrained devices. Therefore, in this paper, we proposed a lattice-based PKE scheme that is based on learning with rounding (LWR) problems. It is a derandomized version of learning with errors (LWE). In LWE, errors are introduced explicitly to mask their true value, whereas in LWR, errors are introduced implicitly by eliminating the least significant bits after performing the rounding operations. Rounding operation for number r could be defined as $$\lfloor r \rceil $$ . Since this approach compresses the key size as compared to the learning with errors scheme. Therefore, it reduces bandwidth requirement for key exchange and increases the transmission speed.
Hyper-Parameter Tuning in Deep Neural Network Learning

Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.
An Approach using Machine Learning Model for Breast Cancer Prediction

Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.
The Importance of Cybersecurity for Organizations

The purpose of this chapter is to understand the severity of cyberattacks and identify the importance of using cybersecurity to protect organizations from future threats. This chapter will discuss the types of attacks that can occur along with the types of security measures that can be implemented to ensure information remains protected for individuals or organizations. Information regarding cyberattacks in certain businesses, healthcare organizations, and social media will also be discussed to assess and identify the different types of cyber threats. Information from previous research studies and past data will be evaluated to identify the types of cybersecurity that can help different organizations as well as identify and prevent any future threats they could potentially encounter. Further examination of previous and future cyberattacks will also be discussed to understand how those threats occurred, where they come from, and how security can be used to combat them.
The Potential of 5G Technology Applied to Tourism Marketing

5G technology is beginning to be implemented, providing some pilot initiatives and significant development, deserving in the tourism area increased attention from tourism entities that may leverage its potential. In this chapter, the authors first consider the technological advances of 5G, presenting some aspects that tend to be leveraged, namely augmented reality and virtual reality. Next, we explain in general terms the contributions that 5G technology can make to tourism and we present in more detail its performance in the area of transportation, accommodation, events, and cultural tourism. The authors also identify and describe some cases, which may appear as pilot experiences, but which are already successful manifestations of 5G technology and representations of its potential. Thus, the main objectives of this document, through an accessible text, are: (i) to raise the awareness of tourism managers, with theoretical concepts and practical examples, for the need to develop 5G initiatives; (ii) to provide academia with further support for possible empirical research.
Introduction to XAI and Clinical Decision Support

Artificial intelligence (AI) and machine learning (ML) offer significant opportunities in healthcare for innovation with its ability to solve cognitive problems normally requiring human intelligence. However, the potential of ML in healthcare has not been realised to date, with limited existing reports of the clinical and cost benefits that have arisen from their real-world in clinical practice. This is due to the lack of understanding about how some ML models operate and ultimately the way they come to make decisions. Explainable AI (XAI) has emerged as a response to this problem investigating methods and techniques that provide insights into the outcome of an ML model and present it in qualitative understandable terms or visualisations to the stakeholders of the model. This chapter introduces XAI and provides some examples of its use within healthcare.
Human-Centred Acoustic Detection for Smartphone Application in Healthcare

In acoustic sensing techniques, acoustic sensors use the main functionality, namely recording and playing the sound, to explore related research and achieve various applications along with novel user experiences. Acoustic sensing is developed in the research, where human-centred research is a vital branch. Human-based research is crucial to healthcare with excellent privacy protection and long-term support. This chapter presents a comprehensive illustration of human-centred acoustic sensing focusing on two innovative dimensions: vertical direction and horizontal direction, to illustrate and analyse the latest developments. In addition, smartphone-based applications and deep learning approaches are systematically described. This chapter also analyses existing technology, predicts its development trend and provides constructive suggestions for future research.
Video Games, Performance, and Algorithmic Processing of Music

Abstract Video games for which the gameplay interacts with game audio as part of the core gameplay loop often invite players to consider music in new ways. Occasionally, this can lead to using games themselves as analytical tools that emphasize different parts of music than many traditional analytical frameworks. Discussions of rhythm and meter, and comparing multiple performances of the same work to one another, are all facilitated by video games in interesting ways. This chapter discusses Crypt of the Necrodancer, Guitar Hero, and Audiosurf as both musically-engaged video games and as spaces inviting conversations surrounding music theory approachable by an engaged, amateur public.
Beyond Justice

Abstract Most discussions in the field of artificial intelligence (AI) ethics concern the avoidance of individual wrongs like discrimination, the violation of privacy, or algorithmic unfairness. Focusing instead on the collective good of community, this chapter assesses how AI will shape our ability to realize this value in contemporary polities. After characterizing the good of community and defining its societal prerequisites, I assess how AI advances will likely shape it in the future. I sketch some positive effects that AI could have: by providing us with unprecedented powers to control the social world, we could, in principle avoid many currently prevailing community-undermining phenomena. Then I contrast this hopeful vision with AI’s challenges. The chapter documents how its implementation has threatened community in existing, imperfect polities through entrenchment and dispersal and how it has contributed to anti-community pathologies and decommunitarization. Next I look at possible structural long-term shifts and their impact on democratic community, namely “datafication,” “automation” and a “disappearing public sphere.” Each of these phenomena challenge our ability to realize the ideal in rapidly transforming circumstances. Finally, I summarize the argument and points to some policies to both mitigate AI’s threats and harness its possibilities. For some challenges, received policy instruments may be sufficient. But for the most significant ones, democratic polities need to develop and implement genuinely new forms of collective political action to control where and how AI is implemented.
Rhetorical Devices Used by Simon Sinek When Delivering an Inspirational Speech

This study focuses on finding out the rhetorical devices used by Simon Sinek when delivering an inspirational speech and how they affect Simon Sinek’s inspirational speech. In this study, the writer used Harris’ (2018) theory of rhetorical question and Gregory’s (2018) theory of general purposes of speech to analyze the data. The writer employed a descriptive qualitative approach. The findings showed that Simon Sinek uses alliteration, parallelism, antithesis, and simile when delivering his speech. The missing device is metaphor because metaphor allows different interpretations, decreases the level of clarity in a speech. Each rhetorical devices affects the speech differently. Alliteration affects the speech using melody; parallelism with repetition and rhythm; antithesis with contrast, complexity, balance, and emphasis; and simile with vivid image, sense of familiarity, and clarity. Ultimately, rhetorical devices affect the inspirational speech in a positive way as they help amplify the meaning of the speech.
Meeting Students Where They Are

This chapter seeks to explore the myriad ways that libraries can leverage expertise to specifically enhance the services undergraduates receive at a large research university. Partnering with nontraditional campus units can expand on the traditional academic library liaison roles. The chapter highlights the development of three nontraditional campus collaborations with different entities on campus and how they were or are developing: Athletic Academic Affairs, the Experiential Learning Network, and Residence Education. The authors discuss the successes, challenges, and failures faced by the library and the new partners. The goal is to outline the ways other academic libraries can build collaborations of value with academic departments that are often overlooked.
Case Load

In 2016, the University of Washington Health Sciences Library (HSL) launched the Translational Research &amp; Information Lab (TRAIL) in collaboration with four partners from across the campus. Capitalizing on each partner's skills, TRAIL coordinates data and innovation services focused on the needs of medical researchers through clinical data management, research data collection, cohort discovery, and emerging technologies. It includes a renovated, technology-enhanced space that has supported creative projects using virtual reality. Cross-partner collaboration has provided HSL librarians with opportunities to spread their proverbial wings beyond the confines of the library, providing key professional development opportunities and benefiting UW's clinical research community. The chapter will discuss and detail how HSL and its TRAIL partners have developed support models harnessing the skills of librarians to provide core support for clinical and biomedical tools laid the foundation for creative and mutually beneficial projects using new and emerging technologies and learning practices.
Playing in the Same Sandbox

This chapter highlights the long-standing collaboration between a library-based research data services team and an office of research-based research technology and computing team. Understanding that both teams wanted to engage the research community on campus, they developed a strong working relationship. Each team supported the other team's growth through sponsorship, networking, and cross-promotion of the other team's services and expertise. Together the teams secured grants, developed educational trainings, and sponsored large campus-wide data-focused programming. The collaboration helped expand the research support services offered and helped each team find their niche in the research support ecosystem.
Computationally efficient and sub-optimal trajectory planning framework based on trajectory-quality growth rate analysis

A planetary exploration rover has been used for scientific missions or as a precursor for a future manned mission. The rover's autonomous system is managed by a space-qualified, radiation-hardened onboard computer; hence, the processing performance for such a computer is strictly limited, owing to the limitation to power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This study, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such an incremental search is subjected to the trade-off between trajectory optimality and computational burden. Therefore, we introduce the trajectory-quality growth rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain, and the planning stop criterion is estimated. Furthermore, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce the computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Furthermore, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.
Unsupervised machine learning to classify language dimensions to constitute the linguistic complexity of mathematical word problems

The study examines language dimensions of mathematical word problems and the classification of mathematical word problems according to these dimensions with unsupervised machine learning (ML) techniques. Previous research suggests that the language dimensions are important for mathematical word problems because it has an influence on the linguistic complexity of word problems. Depending on the linguistic complexity students can have language obstacles to solve mathematical word problems. A lot of research in mathematics education research focus on the analysis on the linguistic complexity based on theoretical build language dimensions. To date, however it has been unclear what empirical relationship between the linguistic features exist for mathematical word problems. To address this issue, we used unsupervised ML techniques to reveal latent linguistic structures of 17 linguistic features for 342 mathematical word problems and classify them. The models showed that three- and five-dimensional linguistic structures have the highest explanatory power. Additionally, the authors consider a four-dimensional solution. Mathematical word problem from the three-dimensional solution can be classify in two groups, three- and five-dimensional solutions in three groups. The findings revealed latent linguistic structures and groups that could have an implication of the linguistic complexity of mathematical word problems and differ from language dimensions, which are considered theoretically. Therefore, the results indicate for new design principles for interventions and materials for language education in mathematics learning and teaching.
Optical Schrödinger cats with generalized coherent states

Canonical coherent states of a quantum harmonic oscillator have been generalized by requiring the conditions of normalizability, continuity in the label and resolution of the identity operator with a positive weight function. Superpositions of these states are considered in the present scenario as a generalization of the optical Schrödinger cat states. The Fock space is assumed to be canonical or finite-dimensional. The photon number distribution of these generalized Schrödinger cat states departs from the Poisson statistics in various ways for high photon numbers. For small nonvanishing values of the label, the photon number distribution is sub-Poissonian (nonclassical) or super-Poissonian, according to the interference properties. In fact, the sub- or super-Poissonian statistics is determined by the interplay between the relative phase and a critical value of the phase. The photon number distribution is uniquely sub-Poissonian for large values of the label.
Improvements for pedestrian safety application P-Minder

Abstract P-Minder is a lightweight sidewalk segmentation and obstacle detection-based approach designed for phubbers’ safety, which can help phubbers to avoid obstacles and other dangers during phubbing walking. But two issues have been found in previous experiments: P-Minder undifferentiated scores the recognition results and costs high resource occupation during background operation. Therefore, this paper proposes the sector correlation model to classify the recognition and segmentation results for an accurate judgment. Then, the new application architecture design and walking detection model decrease the resource occupation. Besides, the datasets are expanded, and the models are retrained to adapt to the improved architecture. In the same datasets, P-Minder achieves a 1.7 percentage point accuracy improvement during the experiment. The experimental result proves that the segmentation model can reach 81.2% mean intersection over union. Besides, the method finally achieves 75.52% detection accuracy in the experiment with a low computing resources cost.
A study on the discourse strategy of telecommunication fraud based on proximization theory

Drawing on the proximization theory, this study investigated the characteristics and expected effects of proximization strategies in the discourse of telecommunication fraud through the textual analysis of samples selected among 2738 fraudulent texts. The results demonstrated a dominant use of spatial proximization strategy, which imitated the main features of institutional discourse to establish a community of interest with the recipients. Moreover, the use of various verb phrases made the recipients feel the imminence of threats or benefits. Additionally, the urgency and importance of the event created by the temporal proximization strategy also served to establish a cooperative relationship between the fraudsters and the recipients. The axiological proximization strategy served as a complementary tool to facilitate the realization of the above two strategies. This study could help with the identification of fraudulent content in texts and the creative production of the anti-fraud materials.