Reviewerly/exHarmony huggingface dataset:
Development of Classical Aceh History Teaching Materials Based on Heyzine Flipbooks to Increase Learning Creativity at Syiah Kuala University’s Department of History Education

The quality of education can be greatly impacted by the teaching materials used.Technology's rapid advancement also necessitates education that is globally competitive, digitally oriented, and capable of increasing students' creativity.The classical Aceh history learning module based on Heyzine Flipbook at Syiah Kuala University's Department of History Education is a learning tool that is being developed in this study in the hope that the learning process will be more interesting and compatible, thereby increasing students' creativity.This research is developmental, with the objective of discovering a novel idea that can be supported by science.The method chosen for this study is Research and Development (R&D), with the Decide, Design, Development, and Evaluation (DDD-E) model.In the decide stage, field observations and literature studies are used to make decisions.In the design stage, an outline of history learning is made.In the development stage, classical Aceh history teaching materials are made using Heyzine Flipbook.In the evaluation stage, the materials are tested to make sure they function properly.The developed learning tools are tested at the evaluation stage by subject matter experts, media experts, learning experts, and respondents (students).After the development and testing phases, it was determined that the Classical Aceh History learning module based on the Heyzine Flipbook at the Department of History Education of Syiah Kuala University was able to boost student creativity.Thus, universities and institutions can employ the aforementioned development of instructional materials to embrace the digitalization era.
Acceptance Factors and Obstacles for Cryptocurrency Adoption

In spite of all the hype, media attention, and explosion in market valuations, cryptocurrencies have so far failed to find wide acceptance as a means of payment. This has led to a wealth of literature investigating why cryptocurrencies such as Bitcoin failed to establish themselves widely. However, these investigations have generally focused on specific cryptocurrencies and did not highlight which features of cryptocurrencies help or hinder adoption. This paper helps close this gap by conducting a qualitative user study with 960 respondents representative of the German population, obtaining freeform answers on the main adoption factors as well as the main obstacles for cryptocurrencies from both existing and potential users. We identify 33 reasons for and against cryptocurrency adoption, distributed into financial, ideological, benefits-based, technical, acceptance-based, and security-based categories. The contribution of this paper is threefold: We go beyond positive reasons and explicitly consider obstacles to cryptocurrency adoption inside a unified framework. We also identify additional payment system features that differ between different cryptocurrencies and influence their adoption. Thirdly, we identify adoption factors based on perceptions and personalities rather than just measurable features. Therefore, this paper also adds to the ongoing systematization of cryptocurrencies in the current stream of literature on the topic.
A Dynamic and Task-Independent Reward Shaping Approach for Discrete Partially Observable Markov Decision Processes

Agents often need a long time to explore state-action space in order to learn how to act expectedly in Partially Observable Markov Decision Processes (POMDPs). With the reward shaping method, real-time POMDP planning can be guided both in terms of reliability and speed. In this paper, we propose Low Dimensional Policy Graph (LDPG), a new reward shaping method for reducing the dimension of the value function to extract the best state-action pairs. The reward function is then shaped using these key pairs. For accelerating learning speed, we analyze the Transition Function graph to discover significant paths to the learning agent’s goal. Direct comparison on five standard testbeds indicates LDPG brings about the deterministic finding of optimal actions faster regardless of the task type. Our method is shown to reach the goals more quickly (by 41.48 % improvement) and performed 61.57 % better in receiving rewards in the $$ 4 \times 5 \times 2 $$ domain.
Bayesian Optimization over Mixed Type Inputs with Encoding Methods

Traditional Bayesian Optimization (BO) algorithms assume that the objective function is defined over numeric input space. To generalize BO for mixed numeric and categorical inputs, existing approaches mainly model or optimize them separately and thus cannot fully capture the relationship among different types of inputs. The complexity incurred by additional operations for the categorical inputs in these approaches can also reduce the efficiency of BO, especially when facing high-cardinality inputs. In this paper, we revisit the encoding approaches, which transfer categorical inputs to numerical ones to form a concise and easy-to-use BO framework. Specifically, we propose the target mean encoding BO (TmBO) and aggregate encoding BO (AggBO), where TmBO transfers each value of a categorical input based on the outputs corresponding to this value, and AggBO encodes multiple choices of a categorical input through several distinct ranks. Different from the prominent one-hot encoding, both approaches transfer each categorical input into exactly one numerical input and thus avoid severely increasing the dimension of the input space. We demonstrate that TmBO and AggBO are more efficient than existing approaches on several synthetic and real-world optimization tasks.
Academic Performance Prediction Using Imbalance Classification Methods: A Study

An issue of classification may arise when learning classifiers use skewed or imbalanced datasets. In case of an imbalanced dataset, the majority of examples are with one class whereas the other class which is normally considered as the most important class, is however signified by a minor share of instances. By using this type of data, the outcome of machine-learning models would be ineffective. There is a term (High training reliability) used to define preconceptions in between one instance against all other illustrations of the class. In this paper, most important methods used to solve the class imbalance problem i.e. data-level, algorithm-level, hybrid, cost-sensitive learning, deep learning etc. including their advantages and limitations are discussed in detail. Performance and efficiency of classifiers are evaluated using a numerous evaluation metrics such as Accuracy, Precision, Recall, F-Measure.
Argentinian Pre-Service Teachers Designing Virtual Learning Environments for Their Practica

Contributing to the development of digital literacy skills in initial English teacher language education (henceforth, IETLE) can support the use of emerging technologies in future educational settings (Fallon, 2020). In Argentina, there is an urgent need to equip pre-service teachers with skills in the use of information and communication technology (ICT) through systematized teaching across IETLE programs. The main aim of the research project outlined in this chapter was to provide pre-service primary school teachers with the necessary competencies to produce a virtual learning environment (henceforth, VLE). The so-called TPACK model, which combines technological, pedagogical and content knowledge (Mishra & Koehler, 2006), was used as a theoretical framework, and it was integrated into a Teaching English as a Foreign Language Methodology course. The participants of this course were twelve pre-service primary school teachers carrying out their practicum (internship) to become English teachers. Their attitudes to the subdimensions of the TPACK model as well as their strengths and weaknesses were investigated (based upon focus group interviews and a detailed analysis of their created VLE s). This contribution concludes with some suggestions for the creation of VLE s for primary school students and the implementation of the TPACK framework in an IETLE program.
Facilitating Knowledge Transfer during Code Compliance Checking Using Conceptual Graphs

The rules in building codes are composed of language, logic structure, and the embedded knowledge that requires further articulation from rule experts. This greatly increases the complexity of rule interpretation. The conceptual graph (CG) is used as a graph-based representation method to capture such knowledge and transfer it to other actors in the compliance checking process. However, due to its subjectivity, it is difficult to ensure the quality of the CG to achieve minimal knowledge loss. This paper proposes a method using reusable CG patterns to reduce the knowledge loss caused by the opacity of concepts in rules, hence better facilitating knowledge transfer using CGs. The rules and their corresponding CGs are analyzed and compared. An assessment framework is used to assess CGs for correctness, completeness, complexity, and comprehensibility. A group of reusable CG patterns is then generated based on the analysis results of concepts in existing CGs. In a case study, rules selected from two types of building codes are used to validate the proposed methods. The results successfully demonstrate the effectiveness of the CG in facilitating knowledge transfer. Moreover, the comparison of quality between original CGs and improved CGs shows that using reusable CG patterns can effectively ease the creation of new CGs and improve its consistency.
Empowering the middle management: Incorporating data summarisation and visualisation techniques in management information systems

Human–computer interaction (HCI) researchers and practitioners have explored how computers and software can aid users in decision-making. Accurate information is crucial in decision-making; therefore, when designing a management information system (MIS), it is necessary to understand the information needs of the end-users and to incorporate this understanding in a way which allows the users to obtain an accurate and intuitive snapshot of the data. Although most of the day-to-day decisions in various organisations are made by middle management employees (as opposed to executives and senior managers), previous literature has scarcely examined how middle management employees’ decision-making process can be improved through the use of MIS. In this study, we examine the impact of different data summarisation and visualisation layouts on different aspects of the decision-making process taking into consideration the decision-makers’ personal characteristics, and propose to include the results of this examination in the MIS design. To this end, we recruited participants from a crowdsourcing platform. Participants were required to complete a task which simulates middle management employees’ day-to-day operational decision-making scenario. To better understand the effects of participants’ personal characteristics and their visual abilities, participants were asked to answer a personality questionnaire and a questionnaire testing for visual abilities. The results indicate that when looking at the population as a single group, the effect of the data summarisation and visualisation layouts on the decision-making process cannot be discerned. However, when taking into consideration additional user characteristics, the results indicate that the data summarisation and visualisation layouts affect the decisions made.
Relevance feedback for building pooled test collections

Offline evaluation of information retrieval systems depends on test collections. These datasets provide the researchers with a corpus of documents, topics and relevance judgements indicating which documents are relevant for each topic. Gathering the latter is costly, requiring human assessors to judge the documents. Therefore, experts usually judge only a portion of the corpus. The most common approach for selecting that subset is pooling. By intelligently choosing which documents to assess, it is possible to optimise the number of positive labels for a given budget. For this reason, much work has focused on developing techniques to better select which documents from the corpus merit human assessments. In this article, we propose using relevance feedback to prioritise the documents when building new pooled test collections. We explore several state-of-the-art statistical feedback methods for prioritising the documents the algorithm presents to the assessors. A thorough comparison on eight Text Retrieval Conference (TREC) datasets against strong baselines shows that, among other results, our proposals improve in retrieving relevant documents with lower assessment effort than other state-of-the-art adjudicating methods without harming the reliability, fairness and reusability.
Transmedia learning and gender in the context of Italian Licei Classici

This paper analyzes the digital profile of Italian Licei Classici students in terms of gender, focusing on the competences required by transmedia learning strategies. To this end, a quantitative methodology is applied to 400 adolescents, based on a questionnaire composed of indicators in the field of new media literacy. Italian adolescents show positive digital skills and attitudes toward transmedia, higher than the reference values for each indicator; moreover, in terms of gender, the traditional differences are generally neutralized (women show even higher values than men). From the point of view of transmedia learning (adaptable, flexible and not technically demanding), the values in which women are better versed (information skills and transmedia navigation) mean that learning opportunities are more favorable to them. Therefore, the context of licei classici can not only host transmedia learning experiences, but transmedia learning can also be a proposal against the traditional gender digital divide.
Game Simulation of the Choice of Strategy for the Development of the System of Points of Delivery of Orders of Internet Stores

The article focuses on the problem of choosing the optimal strategy for the development of the system of Internet order pick–up points in the context of the dynamics of solvent demand and competition of Internet stores for the market of consumer goods. A game model of the situation of choosing a strategy for the development of a system of base-level delivery points is constructed, which allows quantifying the usefulness of each of the strategies in accordance with the possible values of the number of orders and the value of the average check. From a mathematical point of view, the constructed game model belongs to the class of games with a generalized player «Nature». A study of the game model of choosing a strategy for the development of the system of order pick-up points of Internet stores based on the use of several criteria used in information situations of complete uncertainty. The obtained results of game modeling have received a meaningful interpretation in terms of the economic situation under consideration. From a methodological point of view, the constructed game model can be used as a new component of the content of applied mathematical training of a graduate of the University of Economics.
Exploiting similarity-induced redundancies in correlation topology for channel pruning in deep convolutional neural networks

The paper discusses the high computational costs associated with convolutional neural networks (CNNs) in real-world applications due to their complex structure, primarily in hidden layers. To overcome this issue, the paper proposes a novel channel pruning technique that leverages the correlation topology of feature maps generated by each CNNs layer to construct a network with fewer nodes, reducing computational costs significantly. Redundant channels exhibit a high degree of topological similarity and tend to increase as the number of network layers rises. Removing the channel corresponding to highly correlated feature maps allows retrieval of the ‘base’ set of characteristics needed by subsequent layers. The proposed channel pruning technique provides a promising approach to reducing the computational costs of deep convolutional neural networks while maintaining high performance levels. By designing a network structure optimized for specific input data types, the method results in more efficient and effective machine learning models. The pruning operation requires fine-tuning to optimize network performance, and experiments using X-ray, chest CT, and MNIST images show that the pruned network can eliminate approximately 80% of redundant channels with minimal performance deterioration (maintaining original CNNs performance at 99.2%).
Interpreting vulnerabilities of multi-instance learning to adversarial perturbations

Multi-instance learning (MIL) is a recent machine learning paradigm which is immensely useful in various real-life applications, like image analysis, video anomaly detection, text classification, etc. It is well known that most of the existing machine learning classifiers are highly vulnerable to adversarial perturbations. Since MIL is a weakly supervised learning, where information is available for a set of instances, called bag and not for every instance, adversarial perturbations can be fatal. In this paper, we have proposed two adversarial perturbation methods to analyze the effect of adversarial perturbations to interpret the vulnerabilities of MIL methods. Out of the two algorithms, one can be customized for every bag, and the other is a universal one, which can affect all bags in a given data set and thus has some generalizability. Furthermore, through simulations, we have demonstrated the efficacy of the proposed algorithms in fooling state-of-the-art MIL approaches, such that these models make incorrect predictions regarding the label assigned to the bag. Finally, we have discussed, through experiments, about taking care of these kind of adversarial perturbations through a simple strategy. Source codes are available at https://github.com/InkiInki/MI-UAP.
Falling for phishing attempts: An investigation of individual differences that are associated with behavior in a naturalistic phishing simulation

Social engineering cyber-attacks such as phishing emails pose a serious threat to the safety of many organizations. Given that the effectiveness of these attacks heavily relies on poor human decision making, an improved understanding of the individual characteristics that increase cybersecurity vulnerability could inform more targeted training. The current study aimed to identify whether several factors, including phishing email detection ability, confidence in one's phishing identification decisions, general attitudes towards one's level of responsibility and efficacy, and employee satisfaction and loyalty to the organization, may predict behavior in a naturalistic phishing simulation in an employment setting. We followed up employees of a large organization who had been recently targeted by a phishing simulation and asked them to complete a survey that included a phishing detection task. The employees’ behavior in the phishing simulation was ranked according to its safety: reporting the suspicious email, neither reporting nor clicking on the embedded link, and clicking on the link. We found that fewer years of employment at the organization and lower employee satisfaction and loyalty predicted increasingly unsafe behavior in the simulation. This suggests that newer and unsatisfied employees are most vulnerable to phishing attempts and might benefit most from targeted cybersecurity training.
Augmented futures? Scenarios and implications of augmented reality use in public spaces

Expectations regarding augmented realities (AR) produce a range of anticipatory imaginations, ranging from hyperbolic visions to fears of entangled risks, negative effects and dystopian futures. On the one hand, assessing visions of emerging technologies brings to the fore the political economy of research and development of such technologies. On the other, developing plausible scenarios seeks to stimulate active socio-political negotiations regarding which futures are desirable (or not) and should (not) be pursued. AR technology may become a highly influential shaper of public spaces, and therefore the futures of humanistic and democratic societies. We introduce the methodology and results of an iterative survey-based scenario-building process, collecting highly interdisciplinary perspectives on augmented reality use in public spaces within the next decade. We outline four ‘extreme ends’ of plausible futures: ‘autocratic augmented reality’, ‘big-tech monopoly’, ‘DIY proliferation’ and ‘participatory public space’, concluding with a discussion of what effects these scenarios would have on societies, and a critical reflection on the scenario-building process and its limits.
On the Complete Non-malleability of the Fujisaki-Okamoto Transform

The Fujisaki-Okamoto (FO) transform (CRYPTO 1999 and JoC 2013) turns any weakly (i.e., IND-CPA) secure public-key encryption (PKE) scheme into a strongly (i.e., IND-CCA) secure key encapsulation method (KEM) in the random oracle model (ROM). Recently, the FO transform re-gained momentum as part of CRISTAL-Kyber, selected by the NIST as the PKE winner of the post-quantum cryptography standardization project. Following Fischlin (ICALP 2005), we study the complete non-malleability of KEMs obtained via the FO transform. Intuitively, a KEM is completely non-malleable if no adversary can maul a given public key and ciphertext into a new public key and ciphertext encapsulating a related key for the underlying blockcipher. On the negative side, we find that KEMs derived via FO are not completely non-malleable in general. On the positive side, we show that complete non-malleability holds in the ROM by assuming the underlying PKE scheme meets an additional property, or by a slight tweak of the transformation.
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out

With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions – how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful responses from the IA cause users to delay or reduce subsequent interactions in the short term via an observational study. Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA’s understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA. Our findings highlight the impact of the feedback effect at both the micro and meso levels. We further discuss its macro-level consequences: unsatisfactory interactions continuously reduce the likelihood and diversity of future user engagements in a feedback loop.
What It Is About

Although the term “Metaverse” has existed since the 1990s, it is only since the beginning of 2021 that an increased interest of the public can be observed. This interest varies in different regions of the world, and some globally active corporations have adjusted their entire business strategy as a result of the developments towards a Metaverse. Why this is the case, what exactly lies behind the term, and what steps have been taken so far on the way to the Metaverse?