Reviewerly/exHarmony huggingface dataset:
Penggabungan Metode Fuzzy Simple Additive Weighting dengan Rank Order Centroid sebagai Pendukung Keputusan Penilaian Kemiskinan di Jawa Timur

The purpose of this study is to determine the results of the combination of Fuzzy Simple Additive Weighting with Centroid Rank Order as a support for poverty assessment decisions in East Java Province. Using a combination of Fuzzy Simple Additive Weighting and Rank Order Centroid is expected to provide better results. Decision support systems can help the government to determine the poverty status of a district or city in poverty reduction. Poverty itself is an important problem in life in many provinces. The problem of poverty triggers social problems that are closely related to the quality of education, crime, hunger, and others, which cause indirect disruption of stability and sustainability. Therefore, a decision support system is needed to make it easier for the government to determine the poorest districts or cities so that the government can aid programs or make strategies to reduce poverty. Fuzzy logic is a method that can be used to overcome uncertainty in decision making. One method in fuzzy logic to overcome uncertainty is to use the Fuzzy Simple Additive Weighting method. In the Fuzzy Simple Additive Weighting method, there is a selection of criteria that will later be weighted based on importance or priority using the Rank Order Centroid method. The criteria used in this study are life expectancy (AHH), literacy rate (AMH), per capita expenditure, and population. The membership function used is the Tringular Fuzzy Number representation. In this study, twenty four rules were given which were obtained from giving different weights to each criterion. So that twenty four of the poorest districts or cities were obtained. Then the final result is the name of the most Regency or City from the twenty four results, namely alternatives twenty seven and twenty eight, namely Sampang Regency and Pamekasan Regency which were entitled to assistance programs from the government.
Implementation of System Usablity Scale in Islamic Cultural History Interactive Learning Media Application at Madrasah Ibtidaiyah Negeri Kumbe Merauke

The objective of this research is to create an interactive multimedia application for teaching Islamic Cultural History at Madrasah Ibtidaiyah Negeri Kumbe Merauke. This application aims to address the students' needs resulting from a lack of comprehension during the learning process. While traditional methods like printed books have been used, they fail to provide the varied and direct interaction required for effective learning. The development process follows the waterfall method, and data is collected through interviews, questionnaires, and observation. The system design employs flowchart design, use case diagram, sequence diagram, and activity diagram. For testing, both the Blackbox method and Usability scale are used. The results of the Blackbox testing demonstrate that the functionality and appearance of the Islamic Cultural History learning application are good and meet expectations. Additionally, the usability scale assessment yields a final score of 71.81, which falls within the Good category (grade scale C). Conclusively, the data suggests that the Islamic Cultural History learning application is acceptable and beneficial for enhancing the learning process for students.
C2AADL_Reverse: A Model-Driven Reverse Engineering Approach for Development and Verification of Safety-Critical Software

The safety-critical system communities have been struggling to manage and maintain their legacy software systems because upgrading such systems has been a complex challenge. To overcome or reduce this problem, reverse engineering has been increasingly used in safety-critical systems. This paper proposes C2AADL_Reverse, a model-driven reverse engineering approach for safety-critical software development and verification. C2AADL_Reverse takes multi-task C source code as input, and generates AADL (Architecture Analysis and Design Language) model of the legacy software systems. Compared with the existing works, this paper considers more reversed construction including AADL component structure, behavior, and multi-threaded run-time information. Moreover, two types of activities are proposed to ensure the correctness of C2AADL_Reverse. First, it is necessary to validate the reverse engineering process. Second, the generated AADL models should conform to desired critical properties. We propose the verification of the reverse-engineered AADL model by using UPPAAL to establish component-level properties and the Assume Guarantee REasoning Environment (AGREE) to perform compositional verification of the architecture. This combination of verification tools allows us to iteratively explore design and verification of detailed behavioral models, and to scale formal analysis to large models. In addition, the prototype tool and the evaluation of C2AADL_Reverse using a real-world aerospace case study are presented.
A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic Mahabharata was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. &gt; 0.5, p &lt; 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.
A Study of the Narrative Method and Philosophical Core of Suspense Films - A Case Study of the Practice Suspense Short Film Symmetrical Composition

The tone of suspense films is one of suspense, with a greater focus on breaking stereotypes in artistic production and viewing modes than in the genre in general. This paper focuses on the description and analysis of the short suspense film Symmetrical Composition in terms of both its presentation and its content. On this basis, it focuses on the prominent element of dreams in suspense films and analyses the deep subconscious and desires of human beings contained in dreams in depth. It also uses narrative theory to examine how suspense films can use the idea and mechanism of the dream world to construct more exciting suspense stories. On the other hand, it draws on cognitive construct theory to point out the cognitive difficulties of dream narratives in suspense films, and how techniques such as story lines, logical narratives and detailed cues can be used to make the films exciting to watch without confusing the audience. The paper concludes with a summary of the practical experience of suspense films on how dream narratives are presented and their reference value for academic research.
A Distributed Learning Method for Deep Echo State Network Based on Improved SVD and ADMM

In recent years, echo state network (ESN) has emerged as an effective way in processing the classification and regression of time series. As a combination of deep learning and ESN, deep echo state network (DESN) naturally improves the performance of shallow ESN. However, two key challenges have to be addressed to achieve the training of DESN in edge environments. On the one hand, a high-dimensional reservoir state matrix is generated when training DESN with a large amount of data, which causes to the high computation cost. On the other hand, it probably produces the delay, privacy concerns when transmitting data from distributed edge nodes to a centralized location. To solve the above challenges, we propose a distributed learning method for DESN based on improved singular value decomposition (SVD) and alternating direction method of multipliers (ADMM). First, a decomposition algorithm for reservoir state matrix based on improved SVD is proposed to train DESN only using the main features of reservoir state matrix, thus reduces the computation cost. Second, a distributed training algorithm for DESN based on improved ADMM is proposed by using dispersed data to train DESN fast, which produces the less communication cost. Finally, the experimental results in theoretical and real-world datasets show that the proposed method can effectively reduce the computation and communication costs in the learning process of DESN while suffering an acceptable performance loss, compared with other distributed learning methods.
Towards Frequency Band Explainability in Synthetic Speech Detection

Recent advancements in deep learning techniques have brought remarkable developments in synthetic media generation, leading to the creation of forged contents that are almost indistinguishable from real data. This phenomenon poses a new challenge for the multimedia forensics community, as the misuse of synthetic media can potentially cause adverse consequences. Regarding the audio field, several methods have been proposed to detect synthetic speech, but due to their data-driven nature, their results are often little interpretable. To overcome this limitation, the scientific community is focusing on Explainable AI (XAI) aimed at understanding the critical elements in a speech track that drive the predictions of the detectors. In this work, we address the task of XAI in synthetic speech detection and explore the critical factors that allow us to detect forged tracks generated by unseen techniques. Our results suggest that the artifacts of synthetic speech are contained in specific frequency bands and show how we can make the detection process more accurate by focusing on single spectral bands. We also generalize our findings to other detectors, showing how these can benefit them and improve their final classification performances.
Recurrence-Based Disentanglement for Detecting Adversarial Attacks on Timeseries Classifiers

Time series classifiers based on deep neural networks (DNNs) are highly vulnerable to carefully crafted perturbations called adversarial attacks, which are capable of completely degrading their accuracy. The primary challenge in detecting such adversarial samples is the difficulty in disentangling the underlying signal from the added perturbations. In this work, we propose a novel technique for detecting adversarial attacks against deep timeseries classifiers. Firstly, we show that a recurrence plot (RP) representation can effectively disentangle adversarial perturbations in time series data as local artifacts in the image domain. Secondly, we demonstrate that these artifacts can be easily amplified or suppressed using image morphological operations, without impacting the true signal information. Consequently, the distributions of RP features (before and after morphological operations) do not change for benign samples, while they begin to diverge for adversarial samples. Finally, we train a normalcy model to encode the distribution of RP features of benign samples and employ outlier detection in the parameter space to detect adversarial samples. Evaluations based on four adversarial attacks (FGSM, BIM, MIM and PGD) and on all 85 datasets in the 2015 UCR TS archive, show that the proposed method outperforms the state-of-the-art and is 3.65× faster on average.
Using Deep Neural Networks for Detecting Depression from Speech

Diagnosing depression has become a major concern in the last years that led the research community to try innovative and creative ways of recognizing it. This paper proposes a system that can identify depression from speech samples based on a classification process performed by deep neural networks. The system was tested on the Distress Analysis Interview Corpus of human and computer interviews (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA), two spontaneous speech databases recorded in realistic conditions and in two different languages. The system is able to generalize well on previously unseen data. Improvements have been obtained over other results reported in literature, yielding an unweighted accuracy (UA) of 91.25% and a weighted accuracy (WA) of 92.10% for DAIC-WOZ.
Understanding the value of curation: A survey of researcher perspectives of data curation services from six US institutions

Data curation encompasses a range of actions undertaken to ensure that research data are fit for purpose and available for discovery and reuse, and can help to improve the likelihood that data is more FAIR (Findable, Accessible, Interoperable, and Reusable). The Data Curation Network (DCN) has taken a collaborative approach to data curation, sharing curation expertise across a network of partner institutions and data repositories, and enabling those member institutions to provide expert curation for a wide variety of data types and discipline-specific datasets. This study sought to assess the satisfaction of researchers who had received data curation services, and to learn more about what curation actions were most valued by researchers. By surveying researchers who had deposited data into one of six academic generalist data repositories between 2019–2021, this study set out to collect feedback on the value of curation from the researchers themselves. A total of 568 researchers were surveyed; 42% (238) responded. Respondents were positive in their evaluation of the importance and value of curation, indicating that the participants not only value curation services, but are largely satisfied with the services provided. An overwhelming majority 97% of researchers agreed that data curation adds value to the data sharing process, 96% agreed it was worth the effort, and 90% felt more confident sharing their data due to the curation process. We share these results to provide insights into researchers’ perceptions and experience of data curation, and to contribute evidence of the positive impact of curation on repository depositors. From the perspective of researchers we surveyed, curation is worth the effort, increases their comfort with data sharing, and makes data more findable, accessible, interoperable, and reusable.
Survey on Blockchain Scalability Addressing Techniques

Decentralized, blockchain-based cryptocurrencies have received a lot of interest and have been used extensively in recent years. Blockchain technology, which is rapidly establishing itself as the most revolutionary technology of recent years is attracting the interest of both the private and public sectors. This blockchain technology has been frequently used in decentralized cryptocurrencies like Bitcoin and Ethereum. Blockchain is being widely used for purposes other than cryptocurrencies. Despite these advantages, scalability remains a major barrier to the widespread use of blockchain and thus a problem worth addressing. In this paper, initially, we discuss blockchain’s evolution in four stages, challenges of blockchain technology, and scaling quadrilemma and trilemma. Later we address the challenges associated in achieving the scalability in the blockchain network and review the approaches for addressing blockchain scalability challenges in different layers with different techniques. We envision this paper as a reference for analyzing and conducting research on the scalability of blockchains.
Experimental Characterization of Polarized Light Backscattering in Fog Environments

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions.
SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT

Ontology embedding methods have been popular in recent years, especially when it comes to representation learning algorithms for solving ontology-related tasks. Despite the impact of large language models on knowledge graphs’ related tasks, there has been less focus on adapting these models to construct ontology embeddings that are both semantically relevant and faithful to the ontological structure. In this paper, we present a novel ontology embedding method that encodes ontology classes into a pre-trained SBERT through random walks and then fine-tunes the embeddings using a distance-based regression loss. We benchmark our algorithm on four different datasets across two tasks and show the impact of transfer learning and our distance-based loss on the quality of the embeddings. Our results show that SORBET outperform state-of-the-art ontology embedding techniques for the performed tasks.
Real-Time Semantic Segmentation via a Densely Aggregated Bilateral Network

With the growing demands of applications on online devices, the speed-accuracy trade-off is critical in the semantic segmentation system. Recently, the bilateral segmentation network has shown promising capacity to achieve the balance between favorable accuracy and fast speed, and has become the mainstream backbone in real-time semantic segmentation. Segmentation of target objects relies on high-level semantics, whereas it requires detailed low-level features to model specific local patterns for accurate location. However, the lightweight backbone of bilateral architecture limits the extraction of semantic context and spatial details. And the late fusion of the bilateral streams incurs the insufficient aggregation of semantic context and spatial details. In this article, we propose a densely aggregated bilateral network (DAB-Net) for real-time semantic segmentation. In the context path, a patchwise context enhancement (PCE) module is proposed to efficiently capture the local semantic contextual information from spatialwise and channelwise, respectively. Meanwhile, a context-guided spatial path (CGSP) is designed to exploit more spatial information by encoding finer details from the raw image and the transition from the context path. Finally, with multiple interactions between bilateral branches, the intertwined outputs from bilateral streams are combined in a unified decoder for a final interaction to further enhance the feature representation, which generates the final segmentation prediction. Experimental results on three public benchmarks demonstrate that our proposed method achieves higher accuracy with a limited decay in speed, which performs favorably against state-of-the-art real-time approaches and runs at 31.1 frames/s (FPS) on the high resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2048\ttimes 1024$</tex-math> </inline-formula> . The source code is released at https://github.com/isyangshu/DABNet.
Development of Android-Assisted Sound Wave Experiment Devices as an Effort to Improve High School Students' Motivation and Physics Learning Outcomes

The experimental device of sound wave in high school rarely available. This study conducted to develop a android-assisted sound wave experiment device. The experiment consist of four type of activities such as linear and circular motion effect dopler, interference and resonance. The study utilized the developmental research design by applying the ADDIE Model. The developed experiment device validity obtained from the expert while its practicality conducted by students and physics teacher consist of 10 students and 2 physics teacher. Based on the results of the effectiveness test on students stated that the android-assisted sound wave experimental device was declared effective in increasing motivation and learning outcomes. With an average evaluation of the motivation of the experimental class 85.56 higher than the control class with an average of 76. The average evaluation of the learning outcomes of the experimental class is 85.07 higher than the control class with a value of 75.22. Thus the developed android-assisted sound wave experimental device has been declared valid, practical and effective for use as a learning medium in teaching and learning activities in the classroom to increase learning motivation and student learning outcomes.
Educational Technology Quarterly: a new journal on the future of education in the digital age

The Educational Technology Quarterly (ETQ) is a new Diamond Open Access peer-reviewed journal by the Academy of Cognitive and Natural Sciences. The journal focuses on the ways in which digital technology can enhance education and welcomes research papers on the pedagogical uses of digital technology where the focus is broad enough to be of interest to a wider education community. In addition to empirical work, ETQ also welcomes systematic reviews and meta-analyses that include clear research questions, a framework of analysis, and conclusions that reflect the aims of the paper. The journal is published four times a year in March, June, September, and December. The first issue of ETQ was published in March 2021.
A Domain-Specific Visual Modeling Language for Augmented Reality Applications Using WebXR

Augmented reality (AR) is a technology that overlays digital information onto real-world objects using devices like smartphones, tablets, or head-mounted displays to enrich human comprehension and interaction with the physical environment. The creation of AR software applications requires today advanced coding skills, particularly when aiming to realize complex, multifaceted scenarios. As an alternative, we propose a domain-specific visual modeling language for designing AR scenarios, enabling users to define augmentations and AR workflows graphically. The language has been implemented on the ADOxx metamodeling platform, together with a software engine for running the AR applications using the W3C WebXR Device API for web-based augmented reality. The language and the AR application are demonstrated through a furniture assembly use case. In an initial evaluation, we show, via a comprehensive feature comparison, that the proposed language exhibits a more extensive coverage of AR concepts compared to preceding model-based approaches.