Reviewerly/exHarmony huggingface dataset:
Analysis of Expressive Speech Acts on the 2017 IPSE YouTube Channel about Panel Discussion of Language Education Course Papers Indonesia IPSE students 2017

Speech acts are illocutionary acts that function to express an assessment of the circumstances specified in the illocutionary, for example saying thank you, expressing condolences, and so on. The research objectives to be achieved in this study are: (1) to reveal the types of expressive speech acts contained in the YouTube channel IPSE (International Program on Science Education) 2017 regarding Panel Discussion of Language Education Course Papers, (2) to Identify forms of speech acts in learn Language Education Course Paper Panel. This research is a descriptive study that in the end tries to describe the speech spoken by the object, which is in accordance with the actual picture. In this study, the method used is the referential method. The listening method was used in this study because the data or object used is in the form of spoken language. there are 59 expressive speeches in 7 videos on the YouTube channel IPSE (International Program on Science Education) 2017 regarding the Panel Discussion of Language Education Course Papers. Among them are asking for 7 as many utterances, apologizing as much as 38 38, and criticizing as many as 14 utterances. This research is expected to provide benefits to readers or the public in the form of increasing understanding of expressive speech acts.
Analysis of Single and Multiple Sentences in Education Rubric in Kompas Newspaper

The purpose of this study is to describe single sentences and compound sentences in the education rubric of the Kompas newspaper. In this study a descriptive qualitative method was used which originated from the educational rubric of the Kompas newspaper October 27 ̶ 29 2022. The technology used to provide data is a tool that guides data analysis. The analysis technique of this research uses the process of collecting data using the listening method with note-taking techniques. In this study, apart from the analysis technique according to Moleong, there are four models, namely (1) data collection, (2) data reduction, (3) data presentation, and (4) conclusions. From the results of this study, researchers found several single sentences and compound sentences in the education rubric of the Kompas newspaper.
Application of the AP4M Model Reading Skills Students at Tanah Ombak Padang Reading Park

The application of the AP4M model of reading skills was carried out in the Tanah Ombak reading park based on observations and interviews with the culture and learning methods of the Minangkabaau Natural School students as well as students in the Tanah Ombak reading park. The purpose of the study was to describe the reading ability of the Tanah Ombak Reading Community students. The theory used is the theory by Akaaya (2012), Haromi (2014), and Akin (2014). The methods used in this research are qualitative and quantitative methods. The research subjects were 8 students at Tanah Ombak Reading Park which consisted of 3 students in grade 3, 6 students in grade 4, 4 students in grade 5 and 5 students in grade 6. The results of data analysis showed that the average score obtained by grade 3 students as much as 9.50, the average value obtained by grade 4 students is 8.15, the average value obtained by grade 5 students is 7.50, and the average value obtained by grade 6 students is 8.48. reading with the AP4M model, the students of Tanah Ombak Reading Park are able to read well with a score of 8.29 in the very good category
Application of Visual Media Pop-Up Book in English Learning in Elementary School

It is good if English has been introduced since childhood to Indonesian children so that it will not become a frightening specter for them later. In the 2013 curriculum, elementary schools do not have English as a subject, because English has always been a local content subject. However, the obstacle in teaching English is that there are still many elementary school students who are not interested in this subject. The reason is that English is a new language for them and they don't have sufficient vocabulary mastery. Because of that, during English lessons, the teacher teaches only a few students who pay attention, there are many students who play, they feel bored and unenthusiastic when learning English. This was faced by students of the SD GP in Raanan Baru Village, Motoling District, causing them to be unenthusiastic and less creative when it came to English lessons. Therefore, teachers must pay attention to learning models that are appropriate to their goals, not boring but can motivate students and attract their attention, namely choosing learning media can help improve vocabulary mastery. Therefore, in this study, the writer used Visual media pop-up book to improve English vocabulary mastery of the students of SD GP Raanan Baru. The results of this study showed a significant difference, namely the results of the post-test were better than the pre-test as evidenced by the average post-test (T2) score of 71.2 with a standard deviation of 13.5, while the average value of the pre- test (T1) is 30 and the standard deviation is 11.1
Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition

Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets.
Uncertainty Prediction for Monocular 3D Object Detection

For object detection, capturing the scale of uncertainty is as important as accurate localization. Without understanding uncertainties, self-driving vehicles cannot plan a safe path. Many studies have focused on improving object detection, but relatively little attention has been paid to uncertainty estimation. We present an uncertainty model to predict the standard deviation of bounding box parameters for a monocular 3D object detection model. The uncertainty model is a small, multi-layer perceptron (MLP) that is trained to predict uncertainty for each detected object. In addition, we observe that occlusion information helps predict uncertainty accurately. A new monocular detection model is designed to classify occlusion levels as well as to detect objects. An input vector to the uncertainty model contains bounding box parameters, class probabilities, and occlusion probabilities. To validate predicted uncertainties, actual uncertainties are estimated at the specific predicted uncertainties. The accuracy of the predicted values is evaluated using these estimated actual values. We find that the mean uncertainty error is reduced by 7.1% using the occlusion information. The uncertainty model directly estimates total uncertainty at the absolute scale, which is critical to self-driving systems. Our approach is validated through the KITTI object detection benchmark.
Synthactic Study of Verba Phrases in The Novel Princess Izatunnuha by Salma Izatunnuha

The research "Analysis of Verbal Phrases in Princess Sayaka's novel by Salma Izatunnuha in terms of Syntactic Studies" is motivated because today many novels are written not only from adults, even children and adolescents have dared to express their ideas and creativity in a work, especially works contained in the KKPK (Small-Owned Work) published by DAR! Mizan Where KKPK is a place to put the works of the nation's children to be published in writing, both in the form of novels or short stories. Because the novels or other works contained in the KKPK (Small-Owned Works), are specialized containers for children and adolescents aged 7-12 years, the sentences used pay attention to the sentence patterns that can be understood by their circles. This study aims to analyze the verb phrases found in the 12-year-old Prinsess Sayaka Novel by Salma Izatunnuha. This research belongs to a descriptive qualitative research. Where the data used in this study in the form of a set of words that form a verb phrase in Novel Princess Sayaka. The process of collecting data is by reading, marking and taking notes. The results of this study are that there are several verb phrases in the novel Princess Sayaka, the form of the phrase used is also a word that is often found in everyday life. Based on the results of research conducted along with the discussion, the conclusion is the verb phrase is a phrase in which the central element is a verb or verb. Verbal phrases have 3 types, namely: modification verb phrases, coordinative verb phrases, and apositive verb phrases. Of these three types of verb phrases, in Princess Sayaka's novel there are only modificative and coordinative verb phrases.&#x0D;
Efficient Restoration of Magnetic Resonance Images Corrupted with Impulse Noise using Spatial Constraints based Fuzzy Decision Filter

Magnetic Resonance (MR) images are subject to unavoidable noises during the data acquisition due to imperfections of device components and trade-offs in the scan parameters. The study proposes a two-step Fuzzy Decision-Based Filter (FDBF) as a post-reconstruction technique to mitigate random valued impulse noise from MR images. The FDBF employs a Spatial Fuzzy C-means (SFCM) clustering for detection and an Intensity Based Fuzzy Estimation (IBFE) technique for restoration. Firstly, SFCM integrates the spatial relation of the adjacent pixels into the membership function to form three separate clusters. The IBFE technique leaves the noise-free cluster unaltered while restoring the remaining in the second step. IBFE incorporates neighbor pixel correlation to restore the corrupted pixel leading to edge preservation. To assess the efficacy of the intended method both the quality metrics and the observed quality of the restored images are considered. The suggested detection strategy using SFCM performs very well, up to a 93% corruption level with zero false and miss detection rates even when there is intensity in homogeneity among pixels. Compared to other existing filtering techniques, the proposed two-step restoration method significantly improves the perceived image quality and other image quality metrics of the restored image without obliterating more intricate details and finer structures. FDBF considers the spatial information of the nearby pixels during the detection and restoration processes, which is essential for MR image restoration.
A Similarity Metric of Categorical Distributions that Accounts for the Kinship of Different Categories

Estimating a level of similarity of two objects is a common problem in pattern recognition, clustering and classification. Among these problems can be reviewer recommendation, similar text documents analysis, human pose detection in video, species distribution clustering, recommendation in internet-shops etc. In case of categorical attributes an object is described as a distribution of membership degrees over categories. Similarity metrics of such distributions are usually defined as a superposition of objects’ similarities for each category. Most often it is a sum of similarities in separate categories. In addition to that each category is considered independently and in isolation from the others. Some practical problems have categories that are kinship. Therefore, it is expedient to consider objects’ similarity not only directly, as a similarity between equivalent categories, but it is also necessary to consider an indirect similarity, cross-similarity through kinship categories. It is such similarity metric of two categorical distributions that accounts for the kinship of different categories is proposed in this paper. The metric has two components. The first component is defined as Czekanowski metric. It defines a direct similarity of categorical distributions as a sum of intersection of distributions’ membership degrees of two objects. After the intersection the residuals are accounted for in the second component of the metric. The second metric’s component is defined as element-wise product of two matrices: matrix of residuals composition from membership degrees of two categorical distributions and matrix of categories’ paired kinship. It is assumed that kinship indices for each pair of categories are known. As a result, with a large number of categories the overall noisy contribution from weakly kinship categories is prominent. Therefore, it is proposed to filter the noise and account only for contribution from strongly kinship categories.
A Spectrum of Geoscience Communication: From Dissemination to Participation

Abstract. This article is a written contribution to accompany the 2023 Katia and Maurice Krafft Award from the European Geosciences Union. Though a consideration of my own practice and that of the wider literature, I investigate whether employing creative approaches can enhance the diversification of geosciences and facilitate broader engagement in its research and governance. I propose a spectrum for geoscience communication, spanning from dissemination to participation, and contend that effective communication demands a creative approach, considering the requirements of diverse audiences. I offer practical recommendations and tactics for successful geoscience communication, including audience awareness, transparency, and engagement with varied communities. This article emphasises the significance of fostering increased recognition for science communication within geosciences and promoting wider engagement in its research and governance. It delivers valuable insights for researchers, educators, communicators, and policymakers interested in enhancing their communication skills and connecting with diverse audiences in the geoscience domain.
COOPERATIVE LEARNING ACTIVITIES IN HISTORY CLASSES

The article mentions the problems of using cooperative education in accordance with the modernization processes of history teaching in general education schools. Collaborative education in the teaching of history is interpreted as an important means of developing the level of cognitive activity of students, methodological recommendations are presented within the topic.
Transparency in Medical Artificial Intelligence Systems

Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
Cyber Threat Hunting Case Study using MISP

The growing frequency of cyberattacks invokes the need to develop and implement more efficient security strategies.Traditional preventive security measures are not able to counter incident threats effectively.These traditional approaches are usually based on heuristic, default or periodic signature rules that cannot efficiently prevent and repel more dynamic modern attacks.Threat hunting (TH) is gaining popularity because it helps to uncover the presence of attacker tactics, techniques and procedures (TTPs) within an environment that has not already been discovered by existing technologies.Threat hunting and threat intelligence are two distinct security disciplines, but they have the capacity to be complimentary.Hence, using cyber threat intelligence (CTI) to reinforce the traditional cybersecurity strategies by generating indicators of compromise (IoCs) feeds of the recent emerging cyberattacks can help the organisation mitigate the attacks more effectively and efficiently.The primary aim of this paper is to design an approach that, based on cyber threat intelligence, will improve the cybersecurity defence strategies adopted by organisations.This goal will be achieved through the presentation of an architecture that collects threat information and feeds to security tools.This proposed architecture contains four main components: data aggregation, normalisation and enrichment, integration with the security operation centre (SOC) tools and real-time monitoring of security information and event management (SIEM).After developing and implementing this architecture, we have conducted tests using Malware Information Sharing Platform (MISP) as a CTI platform to collect the threat information regarding the indicators and the Tactics, Techniques and Procedures (TTPs) of the known attack (Muddy Water threat actor).Subsequent tests were also conducted on emerging cyberattacks (SVBMv3 vulnerability and Covid19 themed cyberattacks campaign).The results obtained provide a defence of the in-depth approach of cybersecurity, which mitigates cyberattacks by efficiently using threat intelligence capabilities for emerging cyberattacks and when threat actors are targeting organisations, using the IoCs collected and the tactics, techniques and procedures.
Cloud Security And Electronic Transaction Security:

The insecurity of cloud leads to loss or exposure of vital information thereby threatens online transactions. These unfortunate happenings resulted in financial loss, reduced patronage of cloud services and investment diversions. Therefore, this chapter assesses cloud security and electronic transaction security. The chapter adopts qualitative research method, data were sourced secondarily through content analysis of edited books, peer-reviewed journals and credible online resources. It was descriptive in nature and thematically analyzed. The paper anchors on routine activity theory and broken windows theory. Findings revealed that cloud activities are under internal security threats of the cloud service providers and external threats of other users of cloud services. The chapter recommends proper background checks for the staff of the cloud service providers, public enlightenment on cloud security protocols. This chapter will be useful to individuals, cloud services providers, NGOs and researchers in the field of online crimes.&#x0D;
Changing Rainfall Pattern in Pokhara Valley, Nepal

&#x0D; &#x0D; &#x0D; Pokhara valley lies on the southern slope of the Annapurna Range in the Himalayas, Nepal. The mountain range blocks the monsoon originating from the Bay of Bengal in the monsoon season. Based on the observed rainfall data from 1991 to 2021 AD from the Department of Hydrology and Meteorology (DHM), Government of Nepal (GoN), this study aims to assess the monthly, seasonal, and annual pattern of rainfall in Pokhara. Trend analysis for annual rainfall as well as seasonal and monthly rainfall was performed and a regression method was used to find the rate of increase or decrease of the rainfall. Pre- monsoon and monsoon seasons indicate both an upward and a downward trend, whereas post-monsoon and winter seasons illustrates the exact opposite. However, the average monthly rainfall indicates an upward trend, despite the fact that the annual rainfall exhibits both an increasing and a decreasing trend. According to the research, there are rising (+ve) and falling (-ve) trends for the different stations. Although both stations are very near, the precipitation varies broadly. Such massive variation within the short distance might be due to climate change, local wind effects, outflanking of hydraulic structures, and monsoon patterns.&#x0D; &#x0D; &#x0D;
Stability of entangled carriers against continuous noise

Similar to the role of carriers in classical communications as a medium for transmitting messages, entangled states can also be considered a medium that plays the role of the information carrier. In this way, we can define protocols with entangled carriers for quantum communications which can also be used for quantum secret-sharing. The outspread of quantum secret-sharing for many users is the substructure of a quantum internet, so it is essential to study such protocols in terms of their practical implementations. Since protocols are performed in noisy environments without interruption, it is necessary to investigate the performance of protocols under continuous noise. This paper studies the stability of these protocols against dephasing and depolarizing noise. It shows that despite the constant effect of noise, the carrier remains in two types of spaces with the entangled basis, which forms complete spaces for the carrier’s qubits. These spaces are compatible with the protocol performance; therefore, the protocol is stable under the noise effect.
New algorithm for determining the number of features for the effective sentiment-classification of text documents

Sentiment analysis of text documents is a very important part of contemporary text mining. The purpose of this article is to present a new technique of text sentiment analysis which can be used with any type of a document-sentiment-classification method. The proposed technique involves feature selection independently of a classifier, which reduces the size of the feature space. Its advantages include intuitiveness and computational noncomplexity. The most important element of the proposed technique is a novel algorithm for the determination of the number of features to be selected sufficient for the effective classification. The algorithm is based on the analysis of the correlation between single features and document labels. A statistical approach, featuring a naive Bayes classifier and logistic regression, was employed to verify the usefulness of the proposed technique. They were applied to three document sets composed of 1,169 opinions of bank clients, obtained in 2020 from a Poland-based bank. The documents were written in Polish. The research demonstrated that reducing the number of terms over 10-fold by means of the proposed algorithm in most cases improves the effectiveness of classification.