Reviewerly/exHarmony huggingface dataset:
Use of Benime Application in Arabic Language Learning

Language is a very basic thing for every human being, not only for communication but also as a forum for the prosecution of knowledge. At this time, other languages are urgently needed because of the increasing circulation of information using other languages, as well as Arabic itself. In the process of learning Arabic, there are several problems or difficulties faced at the IT Ar-Rahman Palangki Elementary School. With the development of technology at this time, therefore technology can be a solution to the difficulties brought into the teaching and learning process of Arabic at SD IT Ar-Rahman Palangki. This is due to the technology that we can use to assist in making interesting learning media, especially applications that can be accessed on Android in the form of images, sounds, and moving animations, such as the Benime application. The benime app itself is an animated video maker app for a more interesting percentage.
From Bits to Qubits: Navigating the Landscape of Modern Electronics

The study delves into the transformative journey of electronics technology. Through a historical overview, it traces the evolution of miniaturization trends and explores the emergence of quantum bits (qubits) with their unique properties of superposition and entanglement. Comparative analysis reveals the advantages of quantum systems in addressing complex challenges. Expert interviews provide practical insights, while the review of experimental findings offers empirical validation of qubitbehavior. Challenges in qubit stability and quantum-classical integration are discussed, emphasizing the crucial role of empirical research in shaping the trajectory of electronics technology.
Effective Methodology and Its Place in Intellectual Culture

Profound changes have taken place in the society of the XXI century. In culture and civilization, the determining role (dominance) passes from science and education to technology and technic. Against the background of the decline in the authority of science and education in society, the situation with the status of philosophy, as well as methodology, has worsened. This provision is due to the fact that the subject of philosophy is knowledge, and for methodology it is scientific knowledge. It is proposed to consider knowledge according to K. Popper’s concept of three worlds. With the giving of a substantial status to knowledge, its origin should no longer be represented as the result of a reflection of reality. Let us define knowledge as an addition to the physical and mental beginnings of a three-component being. In order to show the productivity of the proposed change, a system-cybernetic model of contradiction is given. In the interests of developing an effective methodology, we turn to the concepts of mono- and multi-aspect approaches to any creative work. The situations of ‘not meaningful incompleteness’ are shown, when the researcher does not even think about the varieties of aspects that open up to the specialist when he starts working with the name of the study, and ‘meaningful incompleteness’, when knowing that it is impossible to consider all the variants of the topic, he justifies why he preferred this trajectory. The technologies of the formation of the names of creative works are presented, taking into account the installation on a single-aspect or multi-aspect study. In the course of solving the task of improving the effectiveness of the methodology, the issues of ontological and epistemic characteristics of the subject are touched upon, which is supplemented by direct indications of the division of knowledge into those that form a controlled subsystem – concepts, and those that form a controlling subsystem – categories. Methods for working with categories are formed – categorical system methodology (CSM), theory of dynamic information systems (TDIS, DIS), DIS-technology. CSM and TDIS are not only bases for the development of effective methodological projects, but they are focused on solving the problems of knowledge synthesis, which, in turn, should focus not only on interdisciplinarity, but also on the multi-aspect of the studied. The proposals outlined here are implemented in practice and in the educational process.
Art Didactics and New Technologies in Primary School Field Practice: Augmented Reality Application for Multimodality

This paper is a result of a field application carried out in a public school in the city of Thessaloniki, Greece. We aimed at integrating an augmented reality application in the primary school curriculum. The role of Art Education in nowadays mostly has to do with specific tasks, traditional art methods and materials that will always have their place in the curriculum. Art education is a fundamental aspect of a well-rounded education. It goes beyond merely teaching artistic techniques; it instills valuable life skills, fosters creativity, and contributes to personal growth, cultural understanding, and societal enrichment. Digital technologies, especially in the post covid and postdigital era, have influenced both the education system in general and art didactics as well. The research question of the paper is how new technologies and applications are reinventing and reshaping art education. The use and the study of specific artworks is carried out in a way for the multisensory experience of contemporary cultural reality. The key is to find a true balance between the field art practice and the high-end tech integration. How can we address old school artworks and artistic content in a multimodal way? Can we address a class free lesson in school? By addressing this empirical, applied learning experience, we tried to facilitate student with the technology integration.
Workshop on Open Citations and Open Scholarly Metadata – 5 October 2022

We’re happy to announce the 3rd edition of the &lt;strong&gt;Workshop on Open Citations and Open Scholarly Metadata (WOOC2022)&lt;/strong&gt;, to be held &lt;strong&gt;online on 5 October 2022 (h. 15-18 CEST)&lt;/strong&gt;. It has now been two years since the last edition of the Workshop on Open Citations and Open Scholarly Metadata (WOOC2020) took place as an online event. During that event, the virtual format did not constrain an enriching and stimulating debate, fostered by the distinguished voices of the speakers involved.
Smart Blockchain Bidding System

Abstract: The E-auction is a prevalent e-commerce event that enables bidders to place bids directly on the Internet. Similar to sealed deals, intermediaries are required to facilitate transactions, as third parties play a crucial role in negotiating during the auction between buyers and sellers. However, the trustworthiness of third parties is never guaranteed. This project showcases a bidding framework utilizing blockchain technology. The bidding application is developed using the Advanced Encryption Standard (AES) Algorithm, which specifies the number of transformation rounds required to convert the input, known as plain text, into the final output, known as cipher text
Perspective Chapter: Cloud Lock-in Parameters – Service Adoption and Migration

ICT has been lauded as being revolutionised by cloud computing, which relieves businesses of having to make significant capital investments in ICT while allowing them to connect to incredibly potent computing capabilities over the network. Organisations adopt cloud computing as a way to solve business problems, not technical problems. As such, organisations across Europe are eagerly embracing cloud computing in their operating environments. Understanding cloud lock-in parameters is essential for supporting inter-cloud cooperation and seamless information and data exchange. Achieving vendor-neutral cloud services is a fundamental requirement and a necessary strategy to be fulfilled in order to enable portability. This chapter highlights technical advancements that contribute to the interoperable migration of services in the heterogeneous cloud environment. A set of guidelines and good practices were also collected and discussed, thus providing strategies on how lock-in can be mitigated. Moreover, this chapter provides some recommendations for moving forward with cloud computing adoption. To make sure the migration and integration between on-premise and cloud happen with minimal disruption to business and results in maximum sustainable cost benefit, the chapter’s contribution is also designed to provide new knowledge and greater depth to support organisations around the world to make informed decisions.
Transition Towards Hybrid Learning Environments in Higher Education Institutions

The recent COVID-19 pandemic forced organizations to find new solutions for remote collaboration and refine hybrid education in the context of applied, praxis-based learning. Various tech giants have started investing in more immersive remote communication technologies, such as metaverse. This paper discusses hybrid environments and metaverse in education at Turku University of Applied Sciences (Turku UAS) as the site of the study. The new strategy of the university aims to support people and organizations in a hybrid world where physical and virtual worlds are merged. At Turku UAS, VR environment is seen as a platform that uses social communication for real-life integration through both hands-on skill development and theoretical knowledge transfer. The metaverse technology and collaborative learning environments can be seen as testbeds for future pedagogy and work life. Examples of implementations of interactive technologies in different fields of education are presented. In addition, a revised hybrid education model with future possibilities is introduced.
Requirement Change Prediction Model for Small Software Systems

The software industry plays a vital role in driving technological advancements. Software projects are complex and consist of many components, so change is unavoidable in these projects. The change in software requirements must be predicted early to preserve resources, since it can lead to project failures. This work focuses on small-scale software systems in which requirements are changed gradually. The work provides a probabilistic prediction model, which predicts the probability of changes in software requirement specifications. The first part of the work considers analyzing the changes in software requirements due to certain variables with the help of stakeholders, developers, and experts by the questionnaire method. Then, the proposed model incorporates their knowledge in the Bayesian network as conditional probabilities of independent and dependent variables. The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. The model was evaluated by sensitivity analysis and comparison methods. For a given dataset, the proposed model computed the low state revisions probability to 0.42, and the high state revisions probability to 0.45. Thus, the results proved that the proposed approach can predict the change in the requirements document accurately by outperforming existing models.
Adaptive Bayesian learning for making risk-aware decisions: A case of trauma survival prediction

Decision tree (DT) models provide a transparent approach to prediction of patient's outcomes within a probabilistic framework. Averaging over DT models under certain conditions can deliver reliable estimates of predictive posterior probability distributions, which is of critical importance in the case of predicting an individual patient's outcome. Reliable estimations of the distribution can be achieved within the Bayesian framework using Markov chain Monte Carlo (MCMC) and its Reversible Jump extension enabling DT models to grow to a reasonable size. Existing MCMC strategies however have limited ability to control DT structures and tend to sample overgrown DT models, making unreasonably small partitions, thus deteriorating the uncertainty calibration. This happens because the MCMC explores a DT model parameter space within a limited knowledge of the distribution of data partitions. We propose a new adaptive strategy which overcomes this limitation, and show that in the case of predicting trauma outcomes the number of data partitions can be significantly reduced, so that the unnecessary uncertainty of estimating the predictive posterior density is avoided. The proposed and existing strategies are compared in terms of entropy which, being calculated for predicted posterior distributions, represents the uncertainty in decisions. In this framework, the proposed method has outperformed the existing sampling strategies, so that the unnecessary uncertainty in decisions is efficiently avoided.
pyMune: A Python package for complex clusters detection

We introduce pyMune, an open-source Python library for robust clustering of complex real-world datasets without density cutoff parameters. It implements DenMune (Abbas et al., 2021), a mutual nearest neighbor algorithm that uses dimensionality reduction and approximate nearest neighbor search to identify and expand cluster cores. Noise is removed with a mutual nearest-neighbor voting system. In addition to clustering, pyMune provides classification, visualization, and validation functionalities. It is fully compatible with scikit-learn and has been accepted into the scikit-learn-contrib repository. The code, documentation, and demos are available on GitHub, PyPi, and CodeOcean for easy use and reproducibility.
TopicBubbler: An interactive visual analytics system for cross-level fine-grained exploration of social media data

How to explore the fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address the challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we proposed a new workflow. Under the procedure of the workflow, we construct the Fine-grained Exploration View through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users to collect information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.
Use of mobile learning applications as an innovative method for the teaching of biochemistry

Traditional teaching methods of biochemistry provide effective tools for knowledge transmission, but are considered less engaging with students. Smartphone applications may provide suitable alternatives to compensate for the shortcomings of traditional teaching methods.This study aimed to assess the effectiveness of smartphone applications as a complementary method for learning biochemistry.A total of 32 students, from the College of Applied Medical Sciences, University of Bisha, Saudi Arabia, were recruited. Students used available mobile applications, and their performance was monitored through assignments, presentations, practical evaluations, and pre- and post-tests. A self-administered structured questionnaire was used to survey the students' perceptions. It was validated by students enrolled at the College of Applied Medical Science, interns, and medical educators. It was checked for item appropriateness and comprehensiveness using face and content validity.Around 75% of the students found the mobile applications useful in learning biochemistry, 50% believed that they were easy to use and 100% believed that the breadth of the knowledge presented by these applications was comprehensive. The pedagogical effect of the use of mobile applications in learning biochemistry showed statistically significant differences in student performances post-use and pre-use of mobile applications with P values of 0.000, 0.028, 0.023, and 0.000 for tests, assignments, practical evaluation, and presentations, respectively.Students have a positive perception of the use of mobile applications, as it has significantly improved their academic performance in biochemistry.
SecTKG: A Knowledge Graph for Open-Source Security Tools

As the complexity of cyberattacks continues to increase, multistage combination attacks have become the primary method of attack. Attackers plan and organize a series of attack steps, using various attack tools to achieve specific goals. Extracting knowledge about these tools is of great significance for both defense and tracing of attacks. We have noticed that there is a wealth of security tool-related knowledge within the open-source community, but research in this area is limited. It is challenging to achieve large-scale automated security tool information extraction. To address this, we propose automated knowledge graph construction architecture, named SecTKG, for open-source security tools. Our approach involves designing a security tool ontology model to describe tools, users, and relationships, which guides the extraction of security tool knowledge. In addition, we develop advanced entity recognition and classification methods, ensuring efficient and accurate knowledge extraction. As far as we know, this work is the first to construct the large-scale security tool knowledge graph, containing 4 million entities and 10 million relationships. Furthermore, we investigate the tendencies and particularities of security tools based on the SecTKG and developed a security tool influence-measuring application. The research fills a gap in the field of automated security tools’ knowledge extraction and provides a foundation for future research and practical applications.
Estimation of the Angles of a Robotic Arm with 7-Free Degrees Using an Improved Hybrid ESSA Algorithm

The electro-search algorithm (ESO) encounters challenges arising from its slow convergence rate and propensity to descend into local optima. In this study, a hybrid variant based on simulated annealing (SA), termed electro search simulated annealing (ESSA), is proposed to tackle these issues and surmount the obstacles. SA assists the proposed ESSA in escaping local optima through the cooling process while propelling individuals within the population. As these propelled individuals search for new positions, they engage in exploration and consequently approach the global optimum. This establishes a balance between exploitation and exploration for ESSA. ESSA has been compared with 10 metaheuristic algorithms on 15 benchmark functions with dimensions of 100, 500, and 1,000. The experimental results demonstrate its high-solution accuracy. Moreover, ESSA has been tested in the optimization of a robotic arm, a technology that requires low-error rates in the medical field. The analysis reveals the competitiveness and advantages of the proposed ESSA algorithm.
The SEC Proposed Cybersecurity Infrastructure Rules and New Disclosure Requirements

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Generative retrieval for conversational question answering

Effective passage retrieval is crucial for conversation question answering (QA) but challenging due to the ambiguity of questions. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. However, this architecture is limited in the embedding bottleneck and the dot-product operation. To alleviate these limitations, we propose generative retrieval for conversational QA (GCoQA). GCoQA assigns distinctive identifiers for passages and retrieves passages by generating their identifiers token-by-token via the encoder–decoder architecture. In this generative way, GCoQA eliminates the need for a vector-style index and could attend to crucial tokens of the conversation context at every decoding step. We conduct experiments on three public datasets over a corpus containing about twenty million passages. The results show GCoQA achieves relative improvements of +13.6% in passage retrieval and +42.9% in document retrieval. GCoQA is also efficient in terms of memory usage and inference speed, which only consumes 1/10 of the memory and takes in less than 33% of the time. The code and data are released at https://github.com/liyongqi67/GCoQA.
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images

Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
Performance Evaluation of Turbo Autoencoder with Different Interleavers

This paper evaluates the performance of Turbo Autoencoder (TurboAE), an end-to-end jointly trained neural channel encoder and decoder, for different well-known inter-leavers over additive white Gaussian noise (AWGN) channel. TurboAE and Turbo codes share structural similarities. Therefore, in search of suitable interleavers for TurboAE, it is important to examine the interleavers designed for Turbo codes. In this paper, different interleavers are compared for TurboAE, and it is shown that using interleavers such as Quadratic Permutation Polynomial (QPP) and S-random interleavers provides considerable performance gain. The interleaver gain is achieved for both TurboAE made of convolutional neural network (CNN) layers and TurboAE made of recurrent neural networks (RNN) layers. Certain techniques are also proposed to enhance the performance of TurboAE RNN.
Vibration Detection Based on Multi-Sensor Information Fusion for Industrial Internet of Things

As science and technology continue to progress, the Industrial Internet of Things (IIoT) is playing an increasingly pivotal role. However, the complexity of the industrial scene has resulted in some IIoT algorithms for vibration detection facing issues such as incomplete waveforms caused by fixed-length data fragments, low accuracy of feature extraction and counting, and poor information fusion effects. To address these challenges and ensure timely identification of industrial equipment faults and the safety of industrial production, this paper proposes a multi-sensor feature fusion algorithm. The algorithm ensures the integrity of the waveform through the detection of the head and tail of the waveform, and aligns the time axis of the multi-sensor, and then uses the method of feature fusion to comprehensively determine the number of vibrations according to various elements such as wave crest, wave width, and average energy, so as to realize the multi-sensor information fusion based on the IIoT. The results show that the algorithm in this paper performs pretty well.