Reviewerly/exHarmony huggingface dataset:
Investigating Players’ Perceptions of Deceptive Design Practices within a 3D Gameplay Context

Deceptive design practices have been identified and studied in games but, to date, there have been no substantial explorations of deceptive design practices within 3D environments typically found in PC games. These offer a new set of affordances for interacting with the player, and game developers may be able to utilize these in order to shape gameplay experiences. The goal of this work was to explore users’ perceptions of deceptive design present in a popular free-to-play 3D game. A survey was carried out with 259 adult respondents identifying and explaining instances of deceptive design within video clips of gameplay from a popular Roblox game. Thematic analysis of the responses revealed six new categories of deceptive design pattern within a 3D gameplay context: Predatory Monetization , Default to Purchase , UI Misdirection , Emotional Interpersonal Persuasion , Physical Placement , and Narrative Obligation . Through our work we hope to highlight the use of deceptive design both within current 3D games and future 3D gaming environments. This work is particularly important as 3D and VR gaming grow in popularity alongside game publishers increasingly moving towards “freemium” monetization models for income.
Visualizing the Spatio-Temporal Evolution of Gameplay using Storyline Visualization: A Study with League of Legends

Players increasingly adopt a data-driven approach to review and improve their gaming skills. In the wake of this, spatio-temporal visualizations gained popularity but remain challenging to design. Storyline visualizations are unique in the way they integrate time and location information into a single view to show how entity relationships develop over time. We adopt the storyline visualization technique to summarize gameplay for the purpose of post-play review. We demonstrate the method by applying it to League of Legends matches and evaluated it with 39 players of the game in a task-based online study using the triad framework for spatio-temporal queries by Peuquet. Results indicate that players responded positively to the approach and could, by and large, solve tasks well but that time-based tasks proved most challenging and least efficient to solve. Based on our findings, we reflect on possibilities for enhancing the design of storyline visualizations for game-related data analysis.
My Heart Will Go On: Implicitly Increasing Social Connectedness by Visualizing Asynchronous Players’ Heartbeats in VR Games

Social games benefit from social connectedness between players because it improves the gaming experience and increases enjoyment. In virtual reality (VR), various approaches, such as avatars, are developed for multi-player games to increase social connectedness. However, these approaches are lacking in single-player games. To increase social connectedness in such games, our work explores the visualization of physiological data from asynchronous players, i.e., electrocardiogram (ECG). We identified two visualization dimensions, the number of players, and the visualization style, after a design workshop with experts (N=4) and explored them in a single-user virtual escape room game. We spatially and temporally integrated the visualizations and compared two times two visualizations against a baseline condition without visualization in a within-subject lab study (N=34). All but one visualization significantly increased participants’ feelings of social connectedness. Heart icons triggered the strongest feeling of connectedness, understanding, and perceived support in playing the game.
Towards Greater Inclusion and Accessibility for Physically Disabled Players in Location-Based Games

Most research on Location-Based Games (LBGs) has been conducted on non-disabled players, meaning the experiences of disabled players are not well understood and potentially overlooked. As such, this research aimed to address the following questions: 1). What are the barriers and needs of disabled LBGs players? 2). How can LBGs be made more inclusive and accessible? To address these questions, two studies were conducted in the form of an online qualitative questionnaire and a series of co-design workshops. Results articulate the needs, barriers, and desired features of 60 participants. Based on our findings and through the collaborative work with disabled players we articulate 4 guidelines for game designers and developers aimed at improving the accessibility of LBGs: 1) Adding avatar features that better represent disabled characteristics, 2). Providing different input options for phone interactions, 3) Allowing for disabled specific accounts and 4) Introducing a messenger feature to LBGs.
Multiple dependence representation of attention graph convolutional network relation extraction model

Abstract Dependency analysis can better help neural network to capture semantic features in sentences, so as to extract entity relation. Currently, hard pruning strategies and soft pruning strategies based on dependency tree structure coding have been proposed to balance beneficial additional information and adverse interference in extraction tasks. A new model based on graph convolutional networks, which uses a variety of representations describing dependency trees from different perspectives and combining these representations to obtain a better sentence representation for relation classification is proposed. A newly defined module is added, and this module uses the attention mechanism to capture deeper semantic features from the context representation as the global semantic features of the input text, thus helping the model to capture deeper semantic information at the sentence level for relational extraction tasks. In order to get more information about a given entity pair from the input sentence, the authors also model implicit co‐references (references) to entities. This model can extract semantic features related to the relationship between entities from sentences to the maximum extent. The results show that the model in this paper achieves good results on SemEval2010‐Task8 and KBP37 datasets.
Identifying dimensions and components of student support system in virtual learning: A scoping review

A student support system (SSS) has a crucial role in the absorption, retention, and success of students in virtual learning. The purpose of this scoping review was to identify and map the available evidence regarding the dimensions and components of the SSS in virtual learning. This study was conducted in accordance with the methodology of the Joanna Briggs Institute (JBI) for scoping reviews. Our search strategy was based on using search engines, such as MEDLINE, EMBASE, Scopus, WoS, CINAHL, ERIC, PsycINFO, ProQuest, and Google Scholar. The articles were published in renowned medical education journals, including Medical Education, Medical Teacher, and Academic Medicine, and the reference lists of identified and reviewed articles were searched manually. The search results were imported into EndNote X20, and after removing duplicates and screenings, 42 studies met the inclusion criteria and were included in the review. A descriptive- analytical approach was employed, including a numerical count of study characteristics (quantitative) and template analysis (qualitative). Five dimensions were identified in the SSS in virtual learning: types of support, domains of support, stages of support, instigating of support, levels of support, and their components and subcomponents. The findings of this study depict a comprehensive roadmap and have an important contribution to the knowledge body of SSS in virtual learning. We suggest system developers, planners, and higher education officials to improve the quality of virtual learning by applying these findings in their planning and decision-making.
The effects of using the nurse self-concept mobile application on the retention of nursing students

The high dropout rate among nursing students has become a global concern as it leads to a shortage of nurses on the one hand and a negative impact on the professional motivation of nursing students on the other. The present study was conducted to investigate the effects of using the Nurse Self-Concept Mobile Application (NSCMA) on the retention of nursing students.The present study is an experimental work of research with a longitudinal design conducted from April to December 2021. The study population was second-year nursing students in a nursing school in the south of Iran who were randomly divided into an experimental (n = 49) and a control group (n = 50). The assessment was based on a pretest-posttest design and a one-month follow-up. The collected data were analyzed in SPSS v. 21 using descriptive statistics (frequency, percentage, mean, and standard deviation) and analytical statistics (repeated measures). The P value was set <0.05.The means of the students' ages and grades were 21.31 ± 2.43 and 15.30 ± 2.13, respectively. The results showed a statistically significant increase in the experimental group's intent-to-stay mean scores as measured in three stages (P = 0.029), indicating that the intervention enhanced the nursing students' inclination to stay in nursing programs. However, the changes in the control group's mean scores were not statistically significant (P = 0.132).The Nurse Self-Concept Mobile Application proved effective in enhancing nursing students' inclination to complete their education and follow a career as a nurse. Thus, it is essential that nursing students' education include programs designed to enhance the students' self-concept, especially in the first few years of their education. Also, nursing schools should use this software to encourage students to stay in the nursing major.
Comprehensive Analysis of the Artificial Intelligence Approaches for Detecting Misogynistic Mixed-Code Online Content in South Asian Countries

The rise of social media has drastically altered several aspects of daily life and businesses. With all its advantages, the anonymity and lack of accountability social media provides encourages unsavoury individuals to spread hate. Hate targeted towards a particular group, such as women, can have a silencing effect and discourage them from participating in online discourse. In this chapter, the authors review recent studies and toolkits that attempt to tackle the issue of hate speech on online platforms using natural language processing (NLP) techniques. Challenges and shared tasks that are regularly conducted to advance the current state-of-the-art in hate speech detection in English and other under-resourced languages are also reviewed. The comprehensive survey suggests that despite the recent increase in interest in the problem of filtering online hate speech, the field is still in its infancy, specifically the problem of misogyny identification in under-resourced languages.
Empowering Digital Citizenship Through Distance Education

The European Union Digital Education Action Plan addresses the imperative of digital citizenship in the landscape of post-pandemic education. This abstract highlights the plan's focus on equipping learners with the skills to navigate the digital sphere responsibly. Emphasizing digital literacy, online safety, and ethical behavior also the plan aims to foster critical engagement with safer technology by promoting inclusivity and equitable access, it seeks to empower individuals to thrive as responsible digital citizens in an interconnected world.
Assisting pre-delivery firmware quality assessments using ensemble learning

ABSTRACTThis study uses retrospective data for firmware tests as the input data sets to train four machine learning models with embedded standalone classifiers. None of these models provide accurate predictions during validation, so model optimization trials adjust the training-validation data portfolio and hyper parameters for each model. Consequently, only the random forest classifier with the best parametric settings just achieves the 90% prediction accuracy required by the standard. Ensemble learning (EL) is then applied using several combinations over the standalone models, and the EL model using logistic regression as the meta classifier increases the accuracy by 6% (i.e. to 96%), which is sufficient for establishing a predictive system. Using the ‘X-minute’ method, it is further identified that the execution period (also the data sampling period) for the sequential read test workload can be reduced from 30 (in current practice) to 20 minutes and that the predictions are sufficiently accurate for system implementation using the EL model. Applying the similarity confirmation method for each pair of ‘score vectors’ (each of which contains a model’s prediction accuracies), several observations distinguishing the performance and the predictive behavioral patterns of the benchmarked models are further confirmed. The knowledge from this advanced research has implications which may benefit future practice in industry.CO EDITOR-IN-CHIEF: Sun, Hung-MinASSOCIATE EDITOR: Sun, Hung-MinKEYWORDS: Quality controlfirmware testingensemble machine learningprocess re-engineering and optimizationdecision-support systemAI in industry Nomenclature AI=artificial intelligenceAPS=automated predictive systemCD=continuous deliveryCI=continuous integrationCOVID-19=corona-virus disease 2019CSV=comma-separated valuesCWV=criteria weight vectorDDDM (D3M)=data-driven decision-makingDSS=decision support systemsEL=ensemble learningFN=false negativeFP=false positiveFW=firmwareI/O=input and outputk-NN=k nearest neighborsLR=logistic regressionMADM=multi-attribute decision-makingMCDM=multi-criteria decision-makingML=machine learningOWV=opinion weight vectorR&D=research and developmentRF=random forestROV=rand order vectorSCM=similarity confirmation methodSOP=standard operating procedureSSD=solid state driveSV=score vectorSVM=support vector machineTN=true negativeTP=true positiveTTM=time to marketVCS=version control systemDisclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Ministry of Science and Technology, Taiwan (ROC), under grants [MOST-108-2511-H-009-009-MY3, MOST-109-2410-H-992 -015 and MOST-111-2410-H-992-011], each in part.
Elementary School Teacher Training for Digital Citizenship in the English Language Classroom

The chapter is a mixed-method study of knowledge, skills, and beliefs of pre-service elementary school teachers on digital citizenship. The quantitative data was gathered through surveying participants. The intervention in the form of a 30-hour module was introduced to participants to identify a change in perceptions. The qualitative data was obtained through the analysis of artefacts in the form of lesson plans. The author grounds the theoretical underpinning into the theory and principles of digital citizenship. The result of the posttest shows a positive change in the knowledge, skills, and beliefs of pre-service elementary school teachers on digital citizenship.
The Economic Development of Nigeria from 1914 to 2014

&lt;p&gt;There is a common Igbo proverb that says: No elephant is burdened by the weight of its tusks. The current economic problem in our nation is often lamented and cursed as unendurable, as if it is peculiar to the nation alone. Similarly, the fiscal measures introduced by the government are looked upon as hindrances or impediments, which a few may consider as irrelevant and unnecessary impositions. Yet, to look back at our growing up problems as if they are burdens, which we must cast off immediately, or run away from, is to be superficial and incapable of understanding ourselves, an offshoot of our character. The Nigerian economy, whether advancing or otherwise, is the weight that we must bear in our growth towards maturity, and in coming into full being as adults and as a nation. A mother does not complain of the weight of the child she carries on her back. The child is part of the process – the route towards her self-fulfillment.&lt;/p&gt;
Why Nigeria's #EndSARS movement is more than a call to end police brutality

&lt;p&gt;• The #EndSARS campaign isn't merely about Nigeria's corrupt Special Anti-Robbery Squad.&lt;/p&gt; &lt;p&gt;• It is also a broader call for social justice from Nigerian youth in the most populous black nation on Earth.&lt;/p&gt; &lt;p&gt;• The protests, and their fundraising structure, has created a microcosm of a properly functioning nation.&lt;/p&gt;
EFFICIENT GRADIENT-BASED ESTIMATION IN FINITE ECONOMICS’ PROBLEMS

Gradient-based methods (GBMs) for estimating values have stability properties, but the temporal difference (TD) and its modifications’ learning methods are much faster in reinforcement learning (RL). We prove a theorem stating the cause of GBMs being slow and show that the mean square of Bellman error (MSBE) is a not appropriate loss function if its second derivative matrix has a significant determinant. To resolve the problem with MSBE on GBMs we propose residual approximate Gauss-Newton with an outliersplitting method (RANS). This method adds outlier-spitting on gradient methods and learning adapter ideas to residual gradient methods making them more stable from the estimation perspective. We show that it is faster than its residual competitors having the same computation time and competing with TD on the baseline problem of economics (CartPole) in RL that we tested. Further analysis and future contributions are considered to make the result of these methods better on any types of economics, which can be built as a finite set of state-action pairs. After proving that claim, GBMs can be used as a baseline in any types of RL-based problems including finite economics problems.
Preferred Professional Development Methods for Health Science Librarians

ABSTRACTProfessional development, also known as continuing education, is when individuals seek to increase or develop knowledge or skills related to their profession. In a field, such as librarianship that incorporates technology, it is even more important. In health science librarianship, the need for professional development has long been regarded as important in providing quality service. This study’s main objective was to determine the preferred professional development method(s) used by health sciences librarians. A survey was developed using Qualtrics and sent out through various listservs used by health sciences librarians and completed by 159 health sciences librarians. Health science librarians answered questions about use and preference as well as rating the resources on how these methods met their professional development needs. The results of this study provide data and insight on which types of professional development health science librarians prefer and which they find most helpful as they become familiar with the nuances that come with serving as a health science librarian. Having a better understanding of the support needed and proven resources can help health sciences librarians on-board to their job sooner and to advocate for future professional development.KEYWORDS: Continuing educationhealth science librarianshiponline learningprofessional development Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/15323269.2023.2262911
Motivation: A Mute Factor in The Individual Performance Commitment Review Rating of Public-School Teachers

The study was primarily conducted to know if there is a significant relationship between the level of motivation of the teachers and their IPCRF ratings. The findings revealed that the motivation level of Public-school teachers are highly motivated but they are a little bit more motivated by mastery goal than performance goal; on the level of motivation of the Public-school teachers when compared according to the level taught under performance goal orientation, there is no significant difference but there is a significant difference on the level of motivation of the Public-school teachers when compared according to the level taught under mastery goal orientation between the elementary teachers and junior high teachers; on the level of Individual Performance among Public-school teachers focusing on the five Key Results Areas, the teachers best perform in KRA 3 – Curriculum and Planning and least perform in KRA 1 - Content and Pedagogy and KRA2 - Diversity of Learners, and Assessment and Reporting and teachers also needs the greatest increase in their performance rating KRA 5 – Plus Factor, on Level of IPCRF rating per KRA of Public-school teachers as compared according to the level taught, there is no significant difference; on the IPCRF ratings of teachers per KRA there is no significant relationship, the IPCRF rating is not affected by their level of motivation except at KRA 5 that the teacher’s IPCRF rating is affected by the level of Mastery goal-oriented motivation, but weakly affected.
Complexity of Reachability Problems in Neural Networks

In this paper we investigate formal verification problems for Neural Network computations. Various reachability problems will be in the focus, such as: Given symbolic specifications of allowed inputs and outputs in form of Linear Programming instances, one question is whether valid inputs exist such that the given network computes a valid output? Does this property hold for all valid inputs? The former question’s complexity has been investigated recently in [20] by Sälzer and Lange for nets using the Rectified Linear Unit and the identity function as their activation functions. We complement their achievements by showing that the problem is NP-complete for piecewise linear functions with rational coefficients that are not linear, NP-hard for almost all suitable activation functions including non-linear ones that are continuous on an interval, complete for the Existential Theory of the Reals $$\exists \mathbb R$$ for every non-linear polynomial and $$\exists \mathbb R$$ -hard for the exponential function and various sigmoidal functions. For the completeness results, linking the verification tasks with the theory of Constraint Satisfaction Problems turns out helpful.
Quantum walk mixing is faster than classical on periodic lattices

The quantum mixing time is a critical factor affecting the efficiency of quantum sampling and algorithm performance. It refers to the minimum time required for a quantum walk to approach its limiting distribution closely and has implications across the areas of quantum computation. This work focuses on the continuous time quantum walk mixing on a regular graph, evolving according to the unitary map U=eiĀt, where the Hamiltonian Ā is the normalized adjacency matrix of the graph. In [Physical Review A 76, 042306 (2007).], Richter previously showed that this walk mixes in time O(ndlog(d)log(1/ϵ)) with O(log(d)log(1/ϵ)) intermediate measurements when the graph is the d−dimensional periodic lattice Zn×Zn×⋯×Zn. We extend this analysis to the periodic lattice L=Zn1×Zn2×⋯×Znd, relaxing the assumption that ni are identical. We provide two quantum walks on periodic lattices that achieve faster mixing compared to classical random walks: 1. A coordinate-wise quantum walk that mixes in O((∑i=1dni)log(d/ϵ)) time with O(dlog(d/ϵ)) measurements. 2. A continuous-time quantum walk with O(log(1/ϵ)) measurements that conjecturally mixes in O(∑i=1dni(log(n1))2log(1/ϵ)) time. Our results demonstrate a quadratic speedup over the classical mixing time O(dn12log(d/ϵ)) on the generalized periodic lattice L. We have provided analytical evidence and numerical simulations to support the conjectured faster mixing time of the continuous-time quantum walk algorithm. Making progress towards proving the general conjecture that quantum walks on regular graphs mix in O(δ−1/2log(N)log(1/ϵ)) time, where δ is the spectral gap and N is the number of vertices.