Reviewerly/exHarmony huggingface dataset:
KONFLIK BATIN DAN NILAI KARAKTER TOKOH UTAMA DALAM NOVEL LOVE, INTERRUPTED KARYA MAYA LESTARI GF SEBAGAI ALTERNATIF BAHAN AJAR SASTRA DI SMA KELAS XII: KAJIAN PSIKOLOGI SASTRA

This research has the first objective to describe the physical structure of the novel Love, Interrupted by Maya Lestari GF. Second, describe the inner conflict of the main character Aisha contained in the novel Love, Interrupted by Maya Lestari GF. Third, describe the character values contained in the novel Love, Interrupted by Maya Lestari GF. Fourth, describe the suitability of the novel Love, Interrupted by Maya Lestari GF so that it can potentially be an alternative source as a good literature teaching material at the XII grade high school level.This research is studied by using literary psychology because the theory used is in the form of psychological theory. In this study, the research approach used is a psychological research approach because this study examines the psychological aspects of characters in a literary work in the form of a novel. The results of the first research are the Love Interrupted novel by Maya Lestari GF which has a structure like novels in general. Secondly, in the novel Love, Interrupted, there is an inner conflict that is understood by the character Aisha which can be an experience for the readers. The three Love, Interrupted novels by Maya Lestari GF contain 16 values of character education, namely responsibility, social care, environmental care, love of reading, patient/communicative, appreciating achievement, curiosity, democracy, independence, creativity, hard work, discipline. , tolerance, honesty, religious, and toughness. The four Love, Interrupted novels by Maya Lestari GF have the potential to be used as an alternative source of teaching materials containing literature at the XII grade high school level based on the field of conformity. This suitability field consists of. (1) in terms of psychology, this novel can be taught to high school students of class XII because it has a positive side to the character Aisha. (2) contains terms from Minang language and English in order to develop insight and knowledge for class XII high school students. (3) this novel is able to increase the imagination of high school students in class XII by describing the situation through imaginary sentences or fantasies used by the author. (4) novels foster students' curiosity with stories found in novels.
Towards run-time search for real-world multi-agent systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.
Fight Misinformation and Detect Fake News Using Machine Learning Algorithm

Abstract: Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’d like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data. As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, mac Shines can be trained to identify patterns in and relationships between input data and automate routine processes Keywords: Random Forest algorithm, Logistic Regression, Long short-term Memory, ROC curves evaluation
Unsharp masking image enhancement the parallel algorithm based on cross-platform

Abstract In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of the algorithm, the problem of unsharp masking processing is implemented in parallel. Making use of the characteristics of data reuse in the algorithm, the effective allocation and optimization of global memory and constant memory are realized according to the access attributes of data and the characteristics of the OpenCL storage model, and the use efficiency of off-chip memory is improved. Through the data storage access mode, the fast computing local memory access mode is discovered, and the logical data space transformation is used to convert the storage access mode, so as to improve the bandwidth utilization of the on-chip memory. The experimental results show that, compared with the CPU serial algorithm, the OpenCL accelerated unsharp masking image enhancement parallel algorithm greatly reduces the execution time of the algorithm while maintaining the same image quality, and achieves a maximum speedup of 16.71 times. The high performance and platform transplantation of the algorithm on different hardware platforms are realized. It provides a reference method for real-time processing of a large amount of data of high-resolution images for image enhancement.
FORMATION OF CORPORATE COMPETITIVENESS OF FUTURE VOCATIONAL TRAINING TEACHERS IN THE SOCIAL PARTNERSHIP OF THE UNIVERSITY AND INNOVATIVE ENTERPRISES

This article describes the conduct of practical training in manufacturing enterprises, taking into account the rapid development and change of the market economy, integrating the educational process of students with traditional educational standards from students to competitive, mature cadres upon graduation. At the same time, the formation of students' culture in the process of practical training with corporations is highlighted.
HISTORICAL PREREQUISITES FOR MEDIA FORMATION OF ARCHITECTURE AND INFORMATIVENESS OF CITY SPACES

The phenomenon of media formation of architecture and urban spaces as their informative essence is considered. Nowadays media formation in architecture complements the conceptual approach to the creation of new objects, formation and development of urban spaces and helps to maintain communication with historical objects. The genesis of media architecture and its evolution during the human history is studied. Media formation is analyzed on three levels: a level of the city, a level of the public space and the level of architectural objects. Their features are identified. The connection between the media formation of architecture and political, social, cultural events that took place in the life of society has been established. Information is revealed as constant characteristics of architecture and urban spaces. The idea of the semantics of architecture and urban spaces is formed. The role of historical conditions of media formation in modern architectural practice is clarified because there are new requirements to the information essence and role of architecture in the life of society in conditions of modern design.
Pre-processing of datasets with best feature selection and outlier removal techniques for a fair and robust model of software defect prediction

Abstract In software engineering, the most demanding task is to predict soft- ware defects. Usually, for predicting defects machine learning techniques are required. As we know in machine learning, datasets play a vital role on par with code. Before applying these techniques pre-processing of data is required for improving the model training process along with making fair and robust models. Pre-processing of the dataset is also done to achieve better accuracy and minimum execution time. In today’s scenario main challenge is to make the dataset fair with the help of pre-processing. Seeing this fact in this paper, we investigate the result of the data pre-processing with the help of best feature selec- tion along with different outlier removal techniques using four different machine learning classifiers and compared the result with the existing state-of-the-art methods. The techniques used for pre-processing include feature selection which is a Hybrid technique combination of Sequen- tial forward selection(SFS) and Sequential backward selection(SBS) developed by us and outlier removal techniques which are IQR, Z- score, Isolation Forest, and DBSCAN. Our findings suggest that the accuracy for module-based defect prediction increases when we pre- processed the dataset by applying feature selection with the outlier removal technique as compared to the feature selection technique only.
Development of Android-based interactive multimedia on interaction of living things with the environment topic for seventh grade junior high school to improve student's learning motivation

Attractive learning media for studying the interaction of living things with the environment is still needed because students' learning motivation with regard to this topic is still relatively low.The low motivation to learn can be due to by several factors, such as used learning materials are less attractive, teaching still involves the traditional lecture method, lack of use of interesting learning media, and a number of concepts to learn that requires students to do memorize more than to understand.To overcome these problems, it is necessary to develop an interesting learning media so that students' learning motivation on the topic can be enhanced.Hence, this research applies research and development (R&D) methods and Lee and Owens (2004) development model.The average percentage obtained overall is 93%, so it can be concluded that this learning media is valid and feasible to use.
Text Data Labelling using Transformer based Sentence Embeddings and Text Similarity for Text Classification

This paper demonstrates that a lot of time, cost, and complexities can be saved and avoided that would otherwise be used to label the text data for classification purposes. The AI world realizes the importance of labelled data and its use for various NLP applications. Here, we have labelled and categorized close to 6,000 unlabelled samples into five distinct classes. This labelled dataset was further used for multi-class text classification. Data labelling task using transformer-based sentence embeddings and applying cosine-based text similarity threshold saved close to 20-30 days of human efforts and multiple human validations with 98.4% of classes correctly labelled as per business validation. Text classification results obtained using this AI labelled data fetched accuracy score and F1 score of 90%.
SMARTS Programme and Pupils’ Metacognitive Abilities - A Pilot Study

The presented paper focuses on the impact of intervention with the application of the SMARTS program on students’ metacognitive abilities. The metacognitive program SMARTS, which is a product of RILD1 (Research Institute for Learning and Development, Lexington, Massachusetts, the author Lynn Meltzer), was translated, adapted, and pilot-tested in the Slovak educational context conditions. In the form of qualitative intervention case studies, the paper analyses (1) the diagnostic potential of SMARTS revealing deficits in students’ metacognitive abilities (organisationorganization and prioritisationprioritization), (2) an intervention to improve a student’s specific metacognitive ability, (3) outcome (stagnation/progress/regression) of the intervention. The results obtained by direct participatory observation applied in the intervention point to a possible positive impact of the SMARTS program on the observed metacognitive abilities of students.
Logarithmic Merkle and Post Quantum Iot Based Big Data Healthcare Analysis Using Blockchain

Abstract The predominant responsibilities carried out all over the data life cycle, including data acquiring, processing, retrieval, and storage, have described the data management. Blockchain technology assists all of these data management procedures. Blockchain-based data management is outlined in healthcare to provide patients dominance over their data. Their consent permits patients and hospital authorities to control who has access to their data and track who has accessed it. For data processing, intelligent contracts-based data-processing-friendly architecture had designed. Finally, as far as data storage is concerned, data storage time, accuracy, and energy consumption remain the primary concern to be focused and this work proposes a novel method called Logarithmic Merkle Quantum-based Legendre Quadratic (LMQ-LQ) IoT-generated healthcare extensive data analysis using Blockchain. The LMQ-LQ process splits into four sections: First, a peer-to-peer network constructs using Blockchain Logarithmic Merkle Tree. Second, the distributed ledger form using the Post Quantum function with the constructed tree. Third, Legendre Quadratic Residue's consensus mechanism design with valid IoT-generated big data. Finally, an intelligent contract model shoots up automatically upon the execution of a transaction. The performance results of the proposed LMQ-LQ IoT generated healthcare extensive data analysis using Blockchain are evaluated in terms of data storage time, data storage accuracy, and energy consumption, respectively.
Error Reduction in Vision-Based Multirotor Landing System

New applications are continuously appearing with drones as protagonists, but all of them share an essential critical maneuver—landing. New application requirements have led the study of novel landing strategies, in which vision systems have played and continue to play a key role. Generally, the new applications use the control and navigation systems embedded in the aircraft. However, the internal dynamics of these systems, initially focused on other tasks such as the smoothing trajectories between different waypoints, can trigger undesired behaviors. In this paper, we propose a landing system based on monocular vision and navigation information to estimate the helipad global position. In addition, the global estimation system includes a position error correction module by cylinder space transformation and a filtering system with a sliding window. To conclude, the landing system is evaluated with three quality metrics, showing how the proposed correction system together with stationary filtering improves the raw landing system.
Consciousness as Existence

The difference for present purposes between ourselves and stones, chairs and our computers is that we are conscious. The difference is fundamental. Being conscious is sufficient for having a mind in one sense of the word ‘mind’, and being conscious is necessary and fundamental to having a mind in any decent sense. What is this difference between ourselves and stones, chairs and our computers? The question is not meant to imply that there is a conceptual or a nomic barrier in the way of non-biological things being conscious. It may happen one decade that the other minds problem will shoot up the philosophical agenda and get a lot of attention as a result of a wonderful computer attached to perceptual and behavioural mechanisms, and that the thing will in the end be taken as conscious, rightly. Our question is not what things can be conscious, but what the property or nature of consciousness is.
Research on extraction method of external force damage based on multi-temporal remote sensing image

Remote sensing can be used to monitor and warn of the damage of external force caused by human activities such as road construction and building construction along the underground cable. The extraction of external force damage information is mainly based on the results of change detection. Based on the object-oriented change detection method, our study selects the optimal features through multi-scale segmentation, multi-source feature construction and classifies the two-time high-resolution satellite remote sensing images. Using the classification results to establish the hierarchical relationship, the idea of superposition analysis is introduced, and the detection results of ground object changes are obtained by logical difference operation, then the external rupture information is extracted. This method avoids the subjectivity and complexity of the varying threshold method. Our results show that the detection probability is 88.63%, the false detection probability is 11.36%, and the miss detection probability is 4.41%.
Editorial for the Special Issue on Blockchain: Applications, Challenges, and Solutions

Blockchain is believed to have the potential to digitally transform and disrupt industry sectors such as finance, supply chain, healthcare, marketing, and entertainment [...]
Filtering with degenerate observation noise: A stochastic approximation approach

This paper is concerned with a general filtering scheme of a continuous-time dynamic system in which the state is not completely observable. The observation process consists of a function of the state with additive noise. Typically, such noise is assumed to be non-degenerate. In this case, various filtering schemes can be developed. For example, in a linear case, the Kalman–Bucy (KB) filter applies and leads to a recursive filtering equation for the conditional expectation of the state given the observation up to time t. Nevertheless, in applications, only some state variables are directly observable and the rest are not. This gives rise to filtering with degenerate observation noise. In this case, traditional filtering schemes fail. This paper develops a viable scheme to address possible degenerate observation noise. In this paper, we propose a recursive filtering equation in which the gain matrix is a matrix-valued parameter to be determined. We adopt the Monte Carlo training procedure used for deep filtering to determine the best gain matrix. In particular, given the state and observation equations, we generate their Monte Carlo samples. Given a gain matrix, we generate the corresponding state estimation samples, which leads to the error function of the state and its estimation. The problem is to choose the gain matrix to minimize the error function. We develop a stochastic approximation method for such a minimization task; we term the procedure the SA filter, where SA stands for stochastic approximation. We focus on computational experiments and demonstrate the performance of the SA filter and its robustness. We also compare the SA filter with the (extended) Kalman–Bucy filter and the deep filter in both linear and nonlinear models.
Online Examination System

Abstract: The online Examination portal is a web-based application for technical evaluation. The online examination portal not only replaces paperwork but also releases the workload of faculty. Most of the e-examination (Online Examinations) have fixed number of questions without randomizations, they have pool scalability. Online examination system is increasing rapidly with the change in scenario due to the pandemic, also it seems to be an easy, flexible, and secure methodology to carry the examinations effectively. Keywords: RDBMS- Relational Database Management System, LDAP- Lightweight Directory Access Protocol, SRS-Software Requirement Specification, DFD- Data Flow Diagram, GPL- General Public License
Pengembangan Aplikasi Mobile Pemesanan dan Pembayaran Makanan Berbasis Cloud Storage

Most dining place or restaurant generally still uses manual food ordering process, the service will come to the customer to ask for the food to be ordered. However, during this soaring covid pandemic, the owners are afraid of the spread through waiters who come to customers. For this reason, an application will be made that can provide self-service food orders by customers without having to interact with waiters. Most digitalization efforts only focus on the client aspect of the ordering process and not on the processing of those orders (in the kitchen). This research looks to give an alternative digital ordering system that is integrated with not only the process on the client side but also on the kitchen side.
Grave errors: Exploring the influence of motion mechanics on learning outcomes in a virtual cemetery.

Systematic research examining affordances such as navigability are important for understanding how to design virtual learning environments.To explore this, a fully immersive virtual cemetery modeled after Edgar Lee Masters' (1915) fictitious Spoon River Anthology was created to assess how motion mechanics related to participant navigability would influence learning outcomes.A 2 × 2, fully crossed, between-subjects (N = 116) experiment manipulating navigation type (natural walking vs. teleportation) and type of piloting (proximal vs. distal orientations) was tested.Participants used the Oculus Rift S virtual reality (VR) headset with a portable backpack, allowing unimpeded movement to complete the study.Results demonstrated that the type of navigation and piloting orientation differentially influenced learning performance.Specifically, an interaction effect occurred where participants in the natural walking with a distal orientation condition showed significantly higher scores on narrative testing, spatial location, and spatial reconstruction tasks, when compared to the teleportation with distal orientation condition.Implications for how these findings may be used in VR environments where the prime objective is learning are discussed.
APPLICATION OF THE ROTATION MODEL FROM MIXED EDUCATION TECHNOLOGIES IN THE EDUCATIONAL PROCESS IN SCHOOLS

This article describes the application of blended learning technologies based on the Rotation model to increase student learning effectiveness in PIMA schools, the advantages and disadvantages of the application process.
Federated Anomaly Detection over Distributed Data Streams

Sharing of telecommunication network data, for example, even at high aggregation levels, is nowadays highly restricted due to privacy legislation and regulations and other important ethical concerns. It leads to scattering data across institutions, regions, and states, inhibiting the usage of AI methods that could otherwise take advantage of data at scale. It creates the need to build a platform to control such data, build models or perform calculations. In this work, we propose an approach to building the bridge among anomaly detection, federated learning, and data streams. The overarching goal of the work is to detect anomalies in a federated environment over distributed data streams. This work complements the state-of-the-art by adapting the data stream algorithms in a federated learning setting for anomaly detection and by delivering a robust framework and demonstrating the practical feasibility in a real-world distributed deployment scenario.
The role of math in IT education

The Communications website, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Andrei Sukhov considers why and how the foundations of teaching mathematics for information technology specialties need to be revised.