Reviewerly/exHarmony huggingface dataset:
Data Augmentation for Hoax Detection through the Method of Convolutional Neural Network in Indonesian News

The concept of hoax or fake news refers to the intentional spread of false information on social media that aims to confuse and mislead readers to achieve an economic or political agenda. In addition, the increasingly diverse and numerous actors in the field of news writing and dissemination have led to the creation of news articles that need to be recognized whether they are credible or not. Furthermore, hoax can harm the social and political aspects of Indonesian society. Central Connecticut University released a study entitled The World's Most Literate Nations in 2016, where Indonesia ranked 60th out of 61 countries, indicating that Indonesian media literacy still needs to improve in critically evaluating information and distinguishing between fake news and valid news. Based on this description, the research will create the Synonym-Based Data Augmentation for Hoax Detection using the Convolutional Neural Network (CNN ) method and Easy Data Augmentation (EDA). This research resulted in an accuracy of 8,.81, indicating that it can be stated to be accurate in detecting hoax news
Extensive dual tree complex wavelet transform-based image steganography using SVD and CNN subspace

The secret data is hidden behind a cover image using digital image steganography technology that prevents the human visual system (HVS) from identifying it. The proposed image steganography method involves scrambling the secret image and then embedding it within the cover image using the dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) technique. The stego image is generated by applying the inverse of DTCWT on the processed embedded image. The PSNR values for the stego and extracted secret images are 45.51 to 30.08 dB and 45.93 dB, respectively, with an SSIM greater than 0.99 in all cases. In the final step of the proposed method, a convolutional neural network (CNN) is utilized to enhance the quality of the extracted secret image. The effectiveness of the proposed method is confirmed by the comparative results.
Pengembangan Laboratorium Virtual pada Materi Penanaman dan Pewarnaan Bakteri di SMA

The material for culturing and coloring bacteria is abstract, requiring other alternatives to understand the material through a practicum in a virtual laboratory. This development research aims to produce virtual laboratory media for high school students. The media development was carried out using the ADDIE model. Media and material experts' and product validation obtained results in a very good category. The feasibility of this virtual laboratory media was practically seen through the results of the teacher's response which is in the excellent category, and the analysis of the perception of small group students is in a good category. Trials for large groups of students were performed in the two classes of MIA. The obtained result was in an excellent category, with Choen's Kappa index of 0.47 (significant 0.00), indicating a fairly strong and significant agreement on the students' responses in both classes regarding the feasibility of the developed virtual laboratory media. It can be concluded that the virtual laboratory media on culturing and coloring bacteria is feasible to be used in schools as an alternative to practicum learning media.
An efficient hybrid by partitioning approach for extractingmaximal gradual patterns in large databases (MPSGrite)

Since automatic knowledge extraction must be performed in large databases, empirical studies are alreadyshowing an explosion in the search space for generalized patterns and even more so for frequent gradual patterns.In addition to this, we also observe a generation of a very large number of relevant extracted patterns. Being facedwith this problem, many approaches have been developed, with the aim of reducing the size of the search spaceand the waiting time for detection, for end users, of relevant patterns. The objective is to make decisions or refinetheir analyses within a reasonable and realistic time frame. The gradual pattern mining algorithms common in largedatabases are CPU intensive. It is a question for us of proposing a new approach that allows an extraction ofthe maximum frequent gradual patterns based on a technique of partitioning datasets. The new technique leadsto a new, more efficient hybrid algorithm called MSPGrite. The experiments carried out on several sets of knowndatasets justify the proposed approach.
The role of education in the innovative society

Innovative processes in the education system were studied from the point of view of the need to continue its modernization. For the transition of society to an innovative educational regime, the primary task is the specialist's readiness for innovations, where theoretical knowledge (modern psychological and pedagogical conceptual approaches), methodological knowledge (general principles of studying educational phenomena, regularities of the process of socialization of the individual, training, education) and technological knowledge (traditional, innovative educational technologies). The meaning of the concept of "innovation" is revealed. The peculiarities of innovative activity in the field of education are emphasized. It is described by parameters that are important for the innovative potential of a person. The article shows the main components on which the education system and the socio-economic sphere are based. The strategic goal of higher education is interpreted: graduates of higher education institutions should be competitive specialists in the modern labor market. The main tasks of higher education are highlighted. Innovative pedagogical technologies are defined as technologies of use, creation, distribution, and introduction of new technologies. The types of innovative technologies are singled out.
Artificial Intelligence and its implications in Basic Education

The new challenges of the information society demand from the Ecuadorian education a great change in its rigid canons of academic training at all times arise the formats based on artificial intelligence promise to be a very substantial improvement in education for all levels, with a qualitative progress without precedents: To provide the student with an accurate active personalization of their learning according to their requirements, managing to integrate various forms of human interaction possible within the coexistence of their family and social school context framing the information and communication technologies that are perfect allies of daily learning. The challenge of today's school is focused on constituting more adaptive people and with greater emotional domain that within the globalized world require expertise to not be influenced by others is why contextualizing this new millennium gravitates on the pressing need to organize, plan, design, develop and implement digital skills in order to form better people able to understand and develop the technological environment according to their priorities, By implementing the universalization of a digital language supported by programs developed under dimensions of artificial intelligence in education will allow to better understand the profile of students and their shortcomings, it is possible to create plans to organize effective and innovative educational activities that allow students to learn in a practical and theoretical way at the same time contributing to the classroom work where children put into play their acquired skills.
An Empirical Study on Workflows and Security Policies in Popular GitHub Repositories

In open-source projects, anyone can contribute, so it is important to have an active continuous integration and continuous delivery (CI/CD) pipeline in addition to a protocol for reporting security concerns, especially in projects that are widely used and belong to the software supply chain. Many of these projects are hosted on GitHub, where maintainers can create automated workflows using GitHub Actions, introduced in 2019, for inspecting proposed changes to source code and defining a security policy for reporting vulnerabilities. We conduct an empirical study to measure the usage of GitHub workflows and security policies in thousands of popular repositories based on the number of stars. After querying the top one-hundred and top one-thousand repositories from all 181 trending GitHub topics, and the top 4,900 overall repositories, totaling just over 173 thousand projects, we find that 37% of projects have workflows enabled and 7% have a security policy in place. Using the top 60 repositories from each of the 34 most popular programming languages on GitHub, 2,040 projects total, we find that 57% of projects have workflows enabled and 17% have a security policy in place. Furthermore, from those top repositories that have support for GitHub CodeQL static analysis, which performs bug and vulnerability checks, only 13.5% have it enabled; in fact, we find that only 1.7% of the top repositories using Kotlin have an active CodeQL scanning workflow. These results highlight that open-source project maintainers should prioritize configuring workflows, enabling automated static analysis whenever possible, and defining a security policy to prevent vulnerabilities from being introduced or remaining in source code.
DUOINSPECT- Uncovering Duplicates through Powerful Model of Random Forest and XGBoost Classifier

Duplicate question pair detection plays a vital role in improving information retrieval systems and enhancing user experience. In this paper, we present a comprehensive study on duplicate question pair detection utilizing the Quora dataset. We employed machine learning techniques, specifically Random Forest and XGBoost classifiers, to develop accurate models for identifying duplicate question pairs. To improve the performance of the models, we introduced additional features to the dataset, augmenting the original data. By incorporating 22 extra features derived from the raw data, we aimed to capture more nuanced patterns and increase the models' discriminatory power. The Random Forest model achieved a significant improvement, with a performance boost to 89.4% accuracy compared to the initial 73% accuracy. The XGBoost classifier also showed promising results, achieving an accuracy of 73.4% initially and 79.2% after incorporating the additional features. This paper serves as a valuable reference for researchers and practitioners interested in the field of duplicate question pair detection. The findings highlight the effectiveness of Random Forest and XGBoost classifiers in combination with additional features for improving accuracy in this task. The web application provides a practical and user-friendly tool for real- time duplicate question pair detection, offering potential applications in information retrieval systems, chatbots, and question-answering platforms. Key Words: Question Pair Similarity; XGBoost, Random Forest, Machine Learning.
LSG-GAN: Latent space guided generative adversarial network for person pose transfer

Person pose transfer is a popular subject in computer vision tasks and is widely used in image editing, virtual try-on, and game designing. Most of the state-of-the-art models are based on flow fields and parsing maps, focusing on improving the deforming of shapes and textures. However, variances in poses always cause spatial misalignments between the source and target images; therefore, complex textures cannot be generated, especially in invisible regions. To solve this issue, we build a latent space of features to model the implicit correlations between sources and targets, and propose a latent space guided generative adversarial network (LSG-GAN) for pose transfer. The proposed model maps source features to the latent space using a feature-mapping block (FMB), and the results of the FMB are introduced into the backbone using a content-based attention mechanism. The experimental results on the DeepFashion dataset and Market1501 dataset show that the proposed model is more advanced than the existing methods, and the qualitative comparisons indicate that invisible textures can also be recovered in high quality.
Postselected quantum hypothesis testing

We study a variant of quantum hypothesis testing wherein an additional ‘inconclusive’ measurement outcome is added, allowing one to abstain from attempting to discriminate the hypotheses. The error probabilities are then conditioned on a successful attempt, with inconclusive trials disregarded. We completely characterise this task in both the single-shot and asymptotic regimes, providing exact formulas for the optimal error probabilities. In particular, we prove that the asymptotic error exponent of discriminating any two quantum states ρ and σ is given by the Hilbert projective metric Dmax(ρ∥σ)+Dmax(σ∥ρ) in asymmetric hypothesis testing, and by the Thompson metric max {Dmax(ρ∥σ), Dmax(σ∥ρ)} in symmetric hypothesis testing. This endows these two quantities with fundamental operational interpretations in quantum state discrimination. Our findings extend to composite hypothesis testing, where we show that the asymmetric error exponent with respect to any convex set of density matrices is given by a regularisation of the Hilbert projective metric. We apply our results also to quantum channels, showing that no advantage is gained by employing adaptive or even more general discrimination schemes over parallel ones, in both the asymmetric and symmetric settings. Our state discrimination results make use of no properties specific to quantum mechanics and are also valid in general probabilistic theories.
Operator-valued Gaussian processes and their covariance kernels

In this paper, we discuss a new framework for operator-valued Gaussian processes and their covariance kernels. Our emphasis is four-fold: (i) starting with a positive operator-valued measure (POVM) [Formula: see text], we present algorithms for constructing an associated centered, operator-valued, Gaussian process [Formula: see text] with [Formula: see text] as its covariance kernel; (ii) we present different classes of POVMs, and we examine the corresponding classes of operator-valued Gaussian processes [Formula: see text]; (iii) for the operator-valued Gaussian processes [Formula: see text] at hand, and the non-commutative framework, we present the corresponding Itô-integrals; and we (iv) outline features of the operator-valued setting which are different from the more familiar case of scalar-valued Gaussian processes.
Super-Resolution Reconstruction Based on Adaptive Weight Adjustment

In image super-resolution, the existing convolution neural network methods increase the number of network layers and filters to achieve better performance, and seldom consider the influence of different branches in feature extraction on the reconstruction effect, which leads to the problems of blurred details and unclear visual perception. Therefore, we propose an adaptive weight adjustment super-resolution (AWSR) reconstruction model in this paper. The model includes Shallow Feature Extraction (SFE), Information Extraction Enhancement Block (IDEB) and Reconstruction Block (RB). IDEB composed of Adaptive Weight Blocks (AWB) and Channel Linking Layers (CLL) learns a deeper mapping relationship between LR image and HR image by adaptively adjusting the proportions of different branches. It not only saves computational cost, but also improves the expression ability of the model. Meanwhile, the performance of the model is further improved by dimension change in the up-sample block. Especially, the image edge and texture reconstruction effects are obviously improved. Compared with SRNHARB algorithm proposed in 2021, the PSNR values are increased by 0.23[Formula: see text]dB, 0.19[Formula: see text]dB and 0.02[Formula: see text]dB at [Formula: see text] on the Set5 dataset. Moreover, the proposed model has a strong generalization ability, and the reconstructed SR images can achieve satisfactory results.
Fibrational Linguistics (FibLang): Language Acquisition

In this work we show how FibLang, a category-theoretic framework concerned with the interplay between language and meaning, can be used to describe vocabulary acquisition, that is the process with which a speaker acquires new vocabulary (through experience or interaction).We model two different kinds of vocabulary acquisition, which we call 'by example' and 'by paraphrasis'.The former captures the idea of acquiring the meaning of a word by being shown a witness representing that word, as in 'understanding what a cat is, by looking at a cat'.The latter captures the idea of acquiring meaning by listening to some other speaker rephrasing the word with others already known to the learner.We provide a category-theoretic model for vocabulary acquisition by paraphrasis based on the construction of free promonads.We draw parallels between our work and Wittgenstein's dynamical approach to language, commonly known as 'language games'.
Research on Adaptive Scheduling of Production Emergency Resources Based on Blockchain Technology

The current adaptive scheduling of production emergency resources is generally set with a single scheduling structure, and the scheduling function is very variable, leading to increased scheduling congestion.Therefore, the verification and analysis of the adaptive scheduling of production emergency resources based on blockchain technology are proposed.According to the actual adaptive scheduling needs and standard changes, the problem description and adaptive resource scheduling assumptions are carried out, the multi-objective optimal scheduling function is designed, the control of function changes is strengthened, the establishment of the objective function is achieved, the blockchain adaptive resource scheduling model is designed, and the block unit modification is used to achieve emergency resource scheduling.The final test results show that the adaptive scheduling congestion is better controlled below 2. 5 after several measurements and studies, which indicates that this self use scheduling method is more stable, safe, and has strong scheduling pertinence.It is more scientific for the distribution and adjustment of emergency materials, and has practical application value.
A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.
The 3D operating room with unlimited perspective change and remote support

Information and communication technologies combined with extended reality improve diagnostics, medical treatment, and surgical operations courses. Thus, the new generation of devices, which enable displaying of holographic objects, allows visualizing a patient’s internal organs, both anatomical and pathological structures, as interactive 3D objects, based on retrospective 2D images, namely computer tomography (CT) or magnetic resonance imaging (MRI). Remote users can observe ongoing surgery with additional features like standing in front of the operation table, walking around in, and changing the user’s perspective without disrupting the operating doctors. Moreover, the operation can be recorded, then played with additional functionalities—remote pointing and painting, which is important in medical education. The operating surgeon may also ask remotely more experienced operators for consultation, and as a consequence, the number of complications and failed procedures can decrease. It can be also applied to robot surgeries as a first step to remote surgery.
Semantic Information Search with Automatic Ontology Creation in Regulations National Standards for Higher Education in Indonesia

In Indonesia, there are around ten types of legal products that contain higher education regulations. With a large number of articles, more effort is needed when users search for links between one article and another. Based on these problems, it is necessary to have an automatic article representation search system using an automatic ontology. Ontology refers to the hierarchical structure of entities and their relationships. In this paper, the results of the development of an information retrieval system with an automated ontology will be explained. This system describes a process begins with receiving input of higher education regulatory files which are used as data samples Permendikbud No 3 of 2020. Then split the data into articles, paragraphs and contents which are then formed ontologies by building 3 detection functions (Definitive Creation, Compound Creation, and Reference Detection). System output has an accuracy of search results reaching an accuracy of 92.5%.
Neuromorphic Circuitry for Superconducting Optoelectronic Networks

We present superconducting optoelectronic circuits optimized for neuromorphic computing and demonstrate integrated synaptic memories and dendritic computation. The circuits process single-photon presynaptic inputs with analog Josephson circuitry for extreme scalability.
A tool for semantic data interoperability

Over the years, the amount of information available to the public by digital means is growing more and more, however, this growth is done in a fragmented way by several companies. Integrating these data may lead to inconsistencies, and system errors. This integration may be difficult and expensive for companies. Data interoperability is done when data with distinct formats or from multiple sources are processed to generate unified data. This work presents a tool to make automatic alignment of data from multiple sources, integrating with the SUMO ontology so that inferences can be made with the tool
Word Search Puzzle using Multi-Linked Lists

Word Search Puzzle is a game in which a player is supposed to find words in the given grid of characters, by unveiling the clues stated for those words. This paper presentsthe creation of the grid of characters in the Word Search Puzzle using multi-linked list data structure. The primary motivation to create this game using multi-linked list data structure was the efficiency of a linked list to handle data – insertion and deletion of an element to the list. The model which was created using a multi-linked list as described in this paper will now be referred to as Model A, for better understanding. The construction of Model A is described as a 5-step process with the help of algorithms. The time complexities to create the grid for Model A and to access data from it were analyzed and found to be much better than the other existing models in the market.