Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS MULTIMEDIA INTERAKTIF UNTUK MENINGKATKAN KEMAMPUAN MELAKUKAN SHOLAT SISWA SEKOLAH DASAR

The research aims to see how the influence of developing interactive multimedia-based learning media is to improve the ability to pray. The focus of research for fourth-grade students at SD Negeri 115525 Sigambal. The material to be packaged is the primary material and the core of learning to pray. The packaged components are sound audio, visualization of images, videos, animations, and sentences or text. The teaching media section consists of introductions, media information assistance, teaching materials, assignments, and exam questions. The media is equipped with a button that makes the media move between the sheets of teaching media. Research methods R&amp;D or Research and Development. Field test with the Guttman Scale for smooth scoring. The Guttman scale will generate action with a definite value, divided into two choices, yes or no. The effect of developing interactive multimedia-based learning media can improve the ability to pray for fourth-grade students at SD Negeri 115525 Sigambal has been recognized as eligible to be a teaching medium for grade four elementary school students at SD Negeri 115525 Sigambal
Withdrawal Notice: Investigation of the Use of Neural Networks for Diagnosing Breast Cancer on Mammograms

The article has been withdrawn at the request of the authors of the journal Current Medical Imaging. Bentham Science apologizes to the readers of the journal for any inconvenience this may cause. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.
Nonfrontal Expression Recognition in the Wild Based on PRNet Frontalization and Muscle Feature Strengthening

Nonfrontal facial expression recognition in the wild is the key for artificial intelligence and human-computer interaction. However, it is easy to be disturbed when changing head pose. Therefore, this paper presents a face rebuilding method to solve this problem based on PRNet, which can build 3D frontal face for 2D head photo with any pose. However, expression is still difficult to be recognized, because facial features weakened after frontalization, which had been widely reported by previous studies. It can be proved that all muscle parameters in frontalization face are more weakened than those of real face, except muscle moving direction on each small area. Thus, this paper also designed muscle movement rebuilding and intensifying method, and through 3D face contours and Fréchet distance, muscular moving directions on each muscle area are extracted and muscle movement is strengthened following these moving directions to intensify the whole face expression. Through this way, nonfrontal facial expression can be recognized effectively.
Using Augmented Reality in Computing Higher Education

In the past, the only way to receive educational content was through traditional methods, which included engaging the learners in full interaction with non-interactive books. Nowadays, we can generate three-dimensional virtual vision by using high-performance computer graphics, which is called Augmented Reality (AR). This research focuses on investigating a new approach to emerging and integrating computing education with AR technology in Saudi Arabia. The research will follow the design science methodology which is a mixed method approach for data collection and analysis. As design science research is considered to be problem focused research, its main tasks are the illustration of the design problems and evaluation of design solutions.
Pengembangan Bahan Ajar Flip Book Kontekstual Berbasis Android Pada Materi Akuntansi Persediaan

This development research aims to create teaching materials in the form of a contextual flip book on financial accounting subjects, especially Android based inventory accounting materials for Vocational High School class XI for competency in institutional accounting and financial expertise. The development of this flip book was tested by material experts, language experts, and graphic experts, and tested on 20 participants, namely students from class XI accounting and financial institutions at SMKN 1 Geger Kab. Madiun. The development research model used is 4D by Thiagarajan which consists of the define, design, develop stages without the dissemination stage. The analysis technique used in this research is descriptive qualitative and quantitative. The results of the validity analysis obtained the material feasibility of eighty five point two percent, language feasibility of ninety four point three percent and graphics of eighty seven point one percent with an average result of expert validation of eighty eight point eighty seven percent with the predicate very feasible. . The average result of student responses is ninety five point six percent with the very good category. So that the inventory accounting teaching materials developed with a contextual approach are very suitable for use in learning activities.
Service Audit in Filling Annual tax Return Using The Cobit 5 Framework at Tabanan Primary Tax service Office

An application of information technology in a company is important in order to make the company better. The information technology can support the achievement of the company's strategic plan to be aligned with its vision, mission and goals. Tabanan Primary Tax Service Office is a company as the example of the application of information technology for this research. The application in that company needs to be regulated, therefore it can be put to good use. In regulating information technology, an audit is needed which aims to evaluate and ensure compliance with the objective approach of a standard. The service audit in filing annual tax returns by using the COBIT 5 framework at the Tabanan Primary Tax Service Office is a form of audit to find out the IT processes that exist in the office. This audit was conducted to determine the level of maturity of the existing IT processes in the service of filing annual tax returns, whether it has been used properly and optimally based on COBIT 5. The audit analysis process performed to obtain capability results, such as the DSS 02, DSS 03, DSS 05, and APO 12 domains. Recommendations and suggestions for improvement of the results obtained are done using the COBIT 5 standard.
A High-Temperature Digital Image Correlation Method and its Application on Strain Measurement of Film Cooling Holes

In this paper, a digital image correlation (DIC) method is developed and applied on film cooling holes in the submillimeter scale in high temperature. Compared with the traditional DIC method, the speckle patterning method and the optical system are improved. In detail, a kind of high temperature-resistant black paint is selected as the basecoat, and the white ZrO 2 particles are evenly distributed on the specimen using high-pressure splashing method. Besides, to eliminate the radiation effect of the high-temperature specimen, the blue light source is used to illuminate the specimen, and the optical bandpass filter is placed in front of the camera to allow the blue light passing. In order to verify the DIC method, the strain measurement on a specimen with single skew hole is performed. The relative error in high temperature of the maximum strain between the measurement results and the numerical simulation results given by the finite element method (FEM) is 12%. The strain concentration factor of the single skew hole is measured as 1.83. Finally, the developed method is applied to the strain measurement of the structure with multiple film cooling holes in 870 ° C. The X-shape strain distribution can be observed at the hole with maximum stress, which suggests that the strain field of multiple holes has coupling effect. In addition, the strain concentration factor of multiple film cooling holes increases to 2.34.
A Nonverbal Recognition Method to Assist Speech

For people with speech disorders, language therapies usually employ speech exercises performed at home, without close specialized supervision. There are fundamental movements to train speech ability called NonVerbal Praxis (NVP). These movements include blows and tongue snaps, which develop both the neuromotor capacities of people with apraxia and the pronunciation of phonemes. However, current research on NVP recognition has employed standard speech recognition techniques without much success. This paper describes a method to identify NVP under field conditions. The method covers the input audio signal segmentation until the audio segments are classified and scored. Our method was experimented with, and NVP sounds were efficiently classified, which helped guide patients and therapists through home therapy. It also provides valuable information for speech-language professionals to evaluate the patient's therapeutic performance.
A Calculation Method of Fragment Number and Mass Distribution of Blast-fragmentation Warhead based on Neural Network

Abstract The Mott distribution model is analyzed and deduced in this paper. On this basis, a calculation method of fragment number and mass distribution of blast-fragmentation warhead based on Neural Network is proposed. Based on the Mott distribution model, the empirical parameters in the Mott distribution model are determined by using the strong nonlinear mapping ability of BP neural network and the associative memory ability of external stimuli and input information Then, the number and mass distribution of fragments are determined, and the calculation speed and result accuracy are improved.
Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection

Answering questions is a primary goal of many conversational systems or search products. While most current systems have focused on answering questions against structured databases or curated knowledge graphs, on-line community forums or frequently asked questions (FAQ) lists offer an alternative source of information for question answering systems. Automatic duplicate question detection (DQD) is the key technology need for question answering systems to utilize existing online forums like StackExchange. Existing annotations of duplicate questions in such forums are community-driven, making them sparse or even completely missing for many domains. Therefore, it is important to transfer knowledge from related domains and tasks. Recently, contextual embedding models such as BERT have been outperforming many baselines by transferring self-supervised information to downstream tasks. In this paper, we apply BERT to DQD and advance it by unsupervised adaptation to StackExchange domains using self-supervised learning. We show the effectiveness of this adaptation for low-resource settings, where little or no training data is available from the target domain. Our analysis reveals that unsupervised BERT domain adaptation on even small amounts of data boosts the performance of BERT.
Understanding evolutionary induction of decision trees

This paper concerns the evolutionary induction of decision trees (DT)s. Noting that many DTs or their parts reappear during the evolution, a multi-tree concept is introduced. A multi-tree integrates all trees with the same test in the root. The differences between trees (below the root) are saved as variants, while the same DT parts are shared. All multi-trees are stored in an external repository that keeps the entire search history. We show that such a repository can achieve not only the goal of duplicated calculation prevention, but also better understanding of the evolutionary DT induction. The road to finding the best individual is easily retrieved, exploitation vs. exploration can be addressed, or similarities between individuals can be measured.
Decentralized Finance: Regulating Cryptocurrency Exchanges

Global financial markets are in the midst of a transformative movement. The creation of Bitcoin and Facebook’s proposed distribution of Diem mark a watershed moment in the evolution of the financial markets ecosystem. Purportedly, peer-to-peer distributed digital ledger technology eliminates legacy financial market intermediaries such as investment banks, depository banks, exchanges, clearinghouses, and broker-dealers.Yet careful examination reveals that cryptocurrency issuers and the firms that offer secondary market cryptocurrency trading services have not quite lived up to their promise. Notwithstanding cryptoenthusiasts’ calls for disintermediation, evidence reveals that platforms that facilitate cryptocurrency trading frequently employ the long-adopted intermediation practices of their traditional counterparts. In fact, when emerging technologies fail, cryptocoin and token trading platforms partner with and rely on traditional financial services firms. As a result, these platforms face many of the risk-management threats that have plagued conventional financial institutions as well as a host of underexplored threats. Automated or algorithmic trading strategies, accelerated high frequency trading tactics, and sophisticated Ocean’s Eleven-style cyberheists leave crypto investors vulnerable to predatory practices.Early responses to fraud, misconduct, and manipulation emphasize intervention when originators first distribute cryptocurrencies— the initial coin offerings. This Article rejects the dominant regulatory narrative that prioritizes oversight of primary market transactions. Instead, this Article proposes that regulators introduce formal registration obligations for cryptocurrency intermediaries —the exchange platforms that provide a marketplace for secondary market trading. This approach recognizes the dynamic nature of cryptocurrency secondary market actors seeking to achieve disintermediation yet balances the potential benefits of trading intermediaries with normative regulatory goals—protecting investors from fraud, theft, misconduct, and manipulation; enforcing accountability; preserving market integrity; and addressing enterprise and systemic risk management concerns
Deep Learning Schema-based Event Extraction: Literature Review and Current Trends.

Schema-based event extraction is a critical technique to apprehend the essential content of events promptly. With the rapid development of deep learning technology, event extraction technology based on deep learning has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models. We summarize the task definition, paradigm, and models of schema-based event extraction and then discuss each of these in detail. We introduce benchmark datasets that support tests of predictions and evaluation metrics. A comprehensive comparison between different techniques is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.
Composing aleatoric music through interaction

Urban public art is an art exhibition held in public places, contextualized with their surroundings and its audience. Technology is a significant trend in public art due to its connection possibilities with human life, fostering different kinds of interaction. In this way, this work presents an installation proposal consisting of an environment for creating collaborative random music from interaction with mobile devices in public spaces. Everyone participating in the installation is a composer and interaction is a chance agent, although it follows John Cage's composition methods. In order to probe technology, we carried out two pilot studies, followed by a workshop for the installation itself. Those two pilot studies led us to a new version that was put into practice during the workshop. During the workshop, participants' interaction generated fourteen compositions, and the sounds resulting from the collaborative composition were made available to the public through a website.
Multi-time-step Segment Routing based Traffic Engineering Leveraging Traffic Prediction

Based on the concept of source routing, Segment Routing (SR) allows the source or ingress node to inject a sequence of segment labels into the packet header and specify the routing path. Due to the routing flexibility, SR has been widely used to solve traffic engineering problems such as minimizing the maximum link utilization. However, most of the prior works only solve the problem in a single snapshot without considering network traffic dynamics, resulting in frequent traffic reroute. To cope with this problem, we focus on solving the segment routing based traffic engineering problem by taking into account the future traffic changes. We formulate the multi-time-step segment routing problem and leverage traffic prediction to extend the length of routing cycles. Due to the large search space of multi-time-step segment routing problem, we further propose a heuristic algorithm for incrementally recomputing the segment routing paths in sub-second. Through extensive experiments on real backbone network traffic datasets, we show that our proposal can achieve a near-optimal performance in term of maximum link utilization while significantly reducing the number of routing changes.
Biased Random-Key Genetic Algorithm for Structure Learning

The structure learning of Bayesian networks is a NP-hard problem, which cannot be easily solved since it is usually a complex combination optimization problem. Thus, many structure learning algorithms using evolutionary techniques are investigated recently to obtain a reasonable result. However, evolutionary algorithms may suffer from a low accuracy and restricts their applications. In this paper, we apply the Biased Random-Key Genetic Algorithm to solve Bayesian network structure learning problem since this framework is novely designed to solve conventional combination optimization problems. Also, we use a local optimization algorithm as its decoder to improve the performance. Experiments show that our method achieves better performances on the real-world networks than other state-of-art algorithms.
Clustering Techniques to Improve Scalability and Accuracy of Recommender Systems

Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system. Each user or item generally belong to at most one cluster. However, it may so happen that some users (boundary users) present in a particular cluster exhibit higher similarity with the preferences of the users residing in the nearby clusters than the ones present in their own cluster. Therefore, we propose a clustering based scalable recommendation algorithm that has a provision for switching a user from its original cluster to another cluster in order to provide more accurate recommendations. For a user belonging to multiple clusters, we aggregate recommendations from those clusters to which the user belongs in order to produce the final set of recommendations to that user. In this work, we propose two types of clustering, one on the basis of rating and the other on the basis of frequency and then compare their performances. Finally, we explore the applicability of cluster ensembles techniques in the proposed method. Our aim is to develop a recommendation framework that can scale well to handle large datasets without much affecting the recommendation quality. The outcomes of our experiments clearly demonstrate the scalability as well as efficacy of our method. It reduces the runtime of the baseline CF algorithm by a minimum of 58% and a maximum of 90% for MovieLens-10M dataset, and a minimum of 42% and a maximum of 84% for MovieLens-20M dataset. The accuracies of recommendations in terms of F1, MAP and NDCG metrics are also better than the existing clustering based recommender systems.
KText: Arbitrary shape text detection using modified K‐Means

Text detection methods based on grouping characters have emerged and have achieved promising performance. Nevertheless, previous methods that grouped characters by learning the relation of adjacent characters or used the heuristic clustering method with a handcrafted feature are unsuitable for dense, curved, or long texts. An effective manner of grouping characters is proposed by introducing K-Means that is modified by the law of universal gravitation, an outlier detection mechanism and sufficient context information. Based on that, corresponding text detector is presented, named the Text Detector, based on modified K-Means (KText), which can generate the bounding boundary of word-level texts with an arbitrary shape. In the experimental stage, two novel stratagems are presented to replenish character-level annotations to several datasets that provide only word-level annotations. To evaluate the effectiveness of the method, experiments are carried out on three benchmarks, ICDAR2013, ICDAR2015 and Total-Text, which contain horizontal, oriented and curved text. The results show that KText performs more competently than most state-of-the-art text detectors when handling dense texts with an arbitrary shape.
CONTRIVANCE FOR SMART MANUFACTURING INTENSIFIED APPLICATION BASED ON CLOUD COMPUTING

Carrying out clever assembling administrations on plant wide edge gadgets associated with creation hardware effectively so those assembling administrations are pluggable, attachment and-play, and reasonable through the organization is a difficult undertaking and is exceptionally advantageous for working with understanding a shrewd plant. Cloud producing is arising as a key empowering influence for assembling organizations to convey exceptionally adjustable administrations over the Web. This paper expects to research how cloud fabricating frameworks can work with compelling assistance arranged business i.e., smart manufacturing. This framework proposes a cloud-based pluggable assembling administration conspire by utilizing cloud computing with security. By utilizing a two-layer hierarchical architecture of service mechanism, the assembling administrations can be inherent to the type of pluggable application module.