Reviewerly/exHarmony huggingface dataset:
Enhancing the Classification of Pap Smear Images using ENN – TLBO classification Method

The most common cancer among the women younger than 35 in developing countries is cervical cancer. It is a human papilloma virus disease. It should be identified earlier by Pap smear test and treated earlier to avoid the consequences. Pap test a colonoscopy is widely used to check the vagina and the cervix. The Pap smear test is the most effective medical test, but it causes difficulty under the microscope at the point of analysis. Automatic cancer detection is designed to unravel the downside. This identification process involves some image processing methods, such as segmentation, and an improved SVM classification method. In this paper, an efficient Elman Neural Network (ENN) collaborating with Teaching Learning Based Optimization (TLBO) algorithm is proposed to classify cancer using Pap Smear Test images. At first, an input image of Pap smear is converted into grey level from the RGB. The grey level image is preprocessed to eliminate unwanted noise produced and smoothened with Kuan Filter (KF). Active Contour Method (ACM) has been used to segment the identified cells from the Pap smear image. Features such as GLCM, haralick, solidity, shape, and other mathematical features are extracted for improving the accuracy. Classification has done using ENN-TLBO. TLBO is utilized for getting optimal weights during the training phase. Performance evaluation has done through the experimental outcomes where, ENN-TLBO yields good accuracy of 86.6%, than the prevailing algorithms such as Support Vector Machine, Radial Basis Function Neural Network classifiers.
Watermarking Applications of Krawtchouk–Sobolev Type Orthogonal Moments

In this contribution, we consider the sequence {Hn(x;q)}n≥0 of monic polynomials orthogonal with respect to a Sobolev-type inner product involving forward difference operators For the first time in the literature, we apply the non-standard properties of {Hn(x;q)}n≥0 in a watermarking problem. Several differences are found in this watermarking application for the non-standard cases (when j&gt;0) with respect to the standard classical Krawtchouk case λ=μ=0.
Cultural Discourse in Reading Texts of Indonesian Language Proficiency Test

Foreign students studying in Indonesia are enhancing from year to year. As students who do not use their first language in communicating, both in learning and in social relationships, their ability to speak Indonesian requires to be tested. It is essential in order to ensure the continuity of the learning that will be passed. Consequently, the government owing to the Language Development and Cultivation Agency has established an Indonesian Language Proficiency Test (UKBI [Uji Kemahiran Berbahasa Indonesia]), which is a standardization test for proficiency in the language of Indonesian speakers, both native and foreign speakers. Despite being a tool to measure Indonesian language skills, UKBI also has the prospect to be utilized as a medium to encourage the diversity of Indonesian cultures. This effort is considered important to preserve and maintain the identity of the nation from the world’s point of view, particularly against the speed of globalization. By observing various cultural elements in the three "reading texts" at UKBI by way of critical discourse analysis, it can be perceived that the three texts have the opportunity to defend the Indonesian language and culture. Furthermore, due to the test results of 30 foreign students in UKBI in Indonesia, foreign students' understanding and knowledge of the diversity of Indonesian languages and cultures were revealed.
A multi texture feature subset-based approach for Image classification

In artificial intelligence, pattern recognition of images is important aspect of computer visioning.The main objective is to identify the texture patterns based on the availability of features or the information.In this article multiple feature subset based on image classification which further depends on GLCM, Contrast visibility, Spatial Frequency, Energy of Gradients and histogram bins of binary patterns.The proposed method also presents a fusion based decision criterion for the texture classification.The experiments were conducted on real time datasets and the performance is evaluated with single and multiple feature subsets with SVM classifier.The identification parts will be tough due to small joints and occlusions.Identification of Image features is described in this paper with the help of Box Model Based Estimation, Speed up robust features and finally with Optical flow tracking algorithm.
Cross-Modal Object Detection Based on a Knowledge Update

As an important field of computer vision, object detection has been studied extensively in recent years. However, existing object detection methods merely utilize the visual information of the image and fail to mine the high-level semantic information of the object, which leads to great limitations. To take full advantage of multi-source information, a knowledge update-based multimodal object recognition model is proposed in this paper. Specifically, our method initially uses Faster R-CNN to regionalize the image, then applies a transformer-based multimodal encoder to encode visual region features (region-based image features) and textual features (semantic relationships between words) corresponding to pictures. After that, a graph convolutional network (GCN) inference module is introduced to establish a relational network in which the points denote visual and textual region features, and the edges represent their relationships. In addition, based on an external knowledge base, our method further enhances the region-based relationship expression capability through a knowledge update module. In summary, the proposed algorithm not only learns the accurate relationship between objects in different regions of the image, but also benefits from the knowledge update through an external relational database. Experimental results verify the effectiveness of the proposed knowledge update module and the independent reasoning ability of our model.
Asynchronous Announcements

We propose a multi-agent epistemic logic of asynchronous announcements, where truthful announcements are publicly sent but individually received by agents, and in the order in which they were sent. Additional to epistemic modalities the logic contains dynamic modalities for making announcements and for receiving them. What an agent believes is a function of her initial uncertainty and of the announcements she has received. Beliefs need not be truthful, because announcements already made may not yet have been received. As announcements are true when sent, certain message sequences can be ruled out, just like inconsistent cuts in distributed computing. We provide a complete axiomatization for this asynchronous announcement logic ( AA ). It is a reduction system that also demonstrates that any formula in AA is equivalent to one without dynamic modalities, just as for public announcement logic. A detailed example modelling message exchanging processes in distributed computing in AA closes our investigation.
Multimodal Offensive Meme Classification u sing Transformers and BiLSTM

Nowadays memes have become a way in which people express their ideas on social media. These memes can convey various views including offensive ones. Memes can be intended for a personal attack, homophobic abuse, racial abuse, attack on minority etc. The memes are implicit and multi-modal in nature. Here we analyze the meme by categorizing them as offensive or not offensive and this becomes a binary classification problem. We propose a novel offensive meme classification using the transformer-based image encoder, BiLSTM for text with mean pooling as text encoder and a Feed-Forward Network as a classification head. The SwinT + BiLSTM has performed better when compared to the ViT + BiLSTM across all the dimensions. The performance of the models has improved significantly when the contextual embeddings from DistilBert replace the custom embeddings. We have achieved the highest recall of 0.631 by combining outputs of four models using the soft voting technique.
Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity &amp; Sedentary Behavior Guidelines

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.
Blockchain Technologies: A Study of the Future of Education

This paper provides insights into the possible, plausible and desirable futures of using blockchain technologies in education. While several universities and education providers are already beginning to use blockchain technologies for complex record manipulations, accounting, and certification of credentials, a whole-systems approach provides a broader view of the issues, promises and challenges associated with using blockchain technologies in education. The study uses social inquiry and causal layered analysis (CLA) to explore impacts across social, economic, environmental, policy, and values dimensions. Findings suggest the needs for new driving metaphors and myths about the purpose and value of education and its place in society as a public good, challenging traditional narratives of the importance and value of formal education and degrees. The implications of this study include a consideration of wider social factors that become important for implementing transformative change in education, presenting challenges to traditional K-12 and higher education curriculum, outcomes, and infrastructure.
Motivation Discourse of Student's Personality in Educational Activity

The article provides theoretical and methodological aspects of motivating students to study in higher education establishment. It is proved that motivation is the system of complementary motives formed and manifested in the activities. The aim of this article is to study the characteristics of educational motivation of students of higher education with the allocation of leading motives. The article emphasizes that the motives for “learning knowledge” and “mastering the profession” are the leading motives for students. Less significant is the motive “getting a diploma” and that success affects students' learning motivation. Studying motivation is defined as a form of motivation that is a part of studying and educational activities. They are determined the following motives of student learning: cognitive, broad social, pragmatic, professional-axiological, aesthetic, status-positional, communicational, traditional-historical, utilitarian-practical, educational-cognitive, social and personal prestige, unconscious, etc. During the study it was found that success is not always determinative in motivation to study. Respondents’ answers to the open questions of the questionnaire were controversial. Certain personal qualities were called by some students as the ones that promote studying, while the other respondents want to get rid of them. This suggests that motivation in studying is affected by student’s character and his or her personal qualities.
Language Use in EFL Classroom Interaction: A Sociolinguistic Study

Society and language cannot be separated, both have a relationship where society can be formed by the language they use. The diversity of languages used by teachers and students in EFL classroom interaction at MAN Katingan represents the existence of a bilingual or multilingual community. This research was conducted to describe how is the language use in EFL classroom interaction. The type of this research was descriptive research using the qualitative method. The subject were one of the English teachers and the students of the eleventh grade of MAN Katingan. The data collection was done by questionnaire using a google form to students and interview was done to the English teacher. The result showed the use of language at MAN Katingan in EFL classroom interaction was varied enough. Based on the students’ view, teacher and students used 24,7% English, 40,4% Indonesian, 8,4% Banjarese, 0,9% Dayakese, and 26,2% mixed the language or used code-switching and code-mixing in EFL classroom interaction. On the other hand, the teacher stated that she mostly used code-switching and code-mixing. Sometimes, she used the local language to interact with the students in her EFL classroom.Keywords: language use: EFL: classroom interaction: sociolinguistics study
The use of multivariate PCA dataset in identifying the underlying drivers of critical stressors, looking at global problems through a local lens

Palynology-based multivariate datasets including geological, ecological, and geochemical data identified the relative importance of the underlying drivers of critical stressors to coastal wetlands by identifying and distinguishing between fluvial flooding, saline water intrusion, delta switching, and the landward migration of coastal plants. A sediment core was retrieved using a vibracorer from an intermediate marsh in Lake Salvador, Louisiana, USA. X-ray Fluorescence (XRF) quantified fluvial and marine elemental concentrations (Cl, Sr, Ca, Mn, K, Ti, Fe, Zn, Zr, Br). Palynology-based agglomerate hierarchical analysis of thirty-two pollen taxa was employed to define ecological clusters. The implementation of multivariate principal component analysis (PCA) to geochemical and ecological variables inferred the source of sedimentary material by correlating four taxonomic groups (floodplain trees, upland trees, tidal freshwater herbs, and inland herbs) to specific geochemical signatures and facilitated the testing of potential correlations between geo- and hydrological-conditions and the six ecosystems (interdistributary, delta-plain, deltaic lake, bottomland and swamp forests, freshwater marsh, and intermediate marsh) depicted in each PCA biplot. The PCA scores quantified the relative importance of multiple variables. The squared cosine function, which demonstrates the relative importance of a variable for a given observation, was used to estimate the representation of each variable on the principal component biplots. Multivariate statistical datasets can be valuable to any scientist working across the spectrum of environmental and planetary science fields as a means of identifying the relative importance of diverse background parameters in controlling ecological and environmental conditions. This methodology is applicable across both natural and social sciences as a means of distinguishing natural and anthropogenic impacts.
Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction

Pneumonia is a wet lung disease. Pneumonia is generally caused by viruses, bacteria or fungi. Not infrequently Pneumonia can cause death. The K-Nearest Neighbors method is a classification method that uses the majority value from the closest k value category. At this time people are not too worried about pneumonia because this pneumonia has symptoms like a normal cough. However, fast and accurate information from health experts is also very necessary so that pneumonia symptoms can be recognized early and how to deal with them can also be done faster. In this study, researchers will diagnose pneumonia to obtain information quickly about the symptoms of pneumonia. This information will adopt human knowledge into computers designed to solve the problem of identifying pneumonia. In this study, the K-Nearest Neighbors method will be combined with the HOG Extraction Feature to identify pneumonia more accurately. The KNN classification used is Fine KNN, Cosine KNN, and Cubic KNN. Where will be seen how the value of accuracy, precision, recall, and fi-score. The results showed that the classification could run well on the Fine KKN, Cosine KNN, and Cubic KNN methods. Fine KNN has an accuracy rate of 80.67, Cosine KNN has an accuracy rate of 84,93333, and Cubic KNN has an accuracy rate of 83,13333. Fine KNN has precision, recall and f1-score values of 0.794842, 0.923706, and 0.854442. Cosine KNN has precision, recall and f1-score values of 0.803048, 0.954039, and 0.872056. Cubic KNN has precision, recall and f1-score values of 0.73388, 0.964561, and 0.833555. From the test results, positive and negative identification of pneumonia was found to be more accurate with the Cosine KNN classification which reached 84,93333.
A Hybrid and Hierarchical Approach for Spatial Exploration in Dynamic Environments

Exploration in unknown dynamic environments is a challenging problem in an AI system, and current techniques tend to produce irrational exploratory behaviours and fail in obstacle avoidance. To this end, we present a three-tiered hierarchical and modular spatial exploration model that combines the intrinsic motivation integrated deep reinforcement learning (DRL) and rule-based real-time obstacle avoidance approach. We address the spatial exploration problem in two levels on the whole. On the higher level, a DRL based global module learns to determine a distant but easily reachable target that maximizes the current exploration progress. On the lower level, another two-level hierarchical movement controller is used to produce locally smooth and safe movements between targets based on the information of known areas and free space assumption. Experimental results on diverse and challenging 2D dynamic maps show that the proposed model achieves almost 90% coverage and generates smoother trajectories compared with a state-of-the-art IM based DRL and some other heuristic methods on the basis of avoiding obstacles in real time.
Dense Feature Matching Based on Homographic Decomposition

Finding robust and accurate feature matches is a fundamental problem in computer vision.However, incorrect correspondences and suboptimal matching accuracies lead to significant challenges for many real-world applications.In conventional feature matching, corresponding features in an image pair are greedily searched using their descriptor distance.The resulting matching set is then typically used as input for geometric model fitting methods to find an appropriate fundamental matrix and filter out incorrect matches.Unfortunately, this basic approach cannot solve all practical problems, such as fundamental matrix degeneration, matching ambiguities caused by repeated patterns and rejection of initially mismatched features without further reconsideration.In this paper we introduce a novel matching pipeline, which addresses all of the aforementioned challenges at once: First, we perform iterative rematching to give mismatched feature points a further chance for being considered in later processing steps.Thereby, we are searching for inliers that exhibit the same homographic transformation per iteration.The resulting homographic decomposition is used for refining matches, occlusion detection (e.g.due to parallaxes) and extrapolation of additional features in critical image areas.Furthermore, Delaunay triangulation of the matching set is utilized to minimize the repeated pattern problem and to implement focused matching.Doing so, enables us to further increase matching quality by concentrating on local image areas, defined by the triangular mesh.We present and discuss experimental results with multiple real-world matching datasets.Our contributions, besides improving matching recall and precision for image processing applications in general, also relate to use cases in image-based computer graphics.
Coupling Factor and Cost Based Task Clustering Method to Optimize Task Clustering For Scientific Workflows in Cloud Environment

Scientific workflows are large scale loosely coupled submissions that are used by Computational Scientists. They are composed of multiple tasks with dependencies between them and are composed of many fine granular tasks. Task clustering is an optimization method that combines multiple tasks into a single job such that task execution time and system overhead is reduced and thus the whole performance is improved in a cloud environment. Though existing task clustering algorithms has significantly reduced the System overhead, yet dependencies among the tasks are not well-thought-out. This work examines the features of task by which the tasks can be clustered and developed proficient task clustering algorithm. In this work two task clustering ideas were proposed namely Horizontal Coupling Factor (HCF) based clustering and Horizontal Processing Cost (HPC) based Task Clustering. Next, the proposed algorithm have been evaluated and tested for various real world applications and the experiment results shows that the proposed approach suits best for data intensive and Compute intensive applications. The obtained results showed that the HCF and HPC task clustering strategies can significantly improve the performance by reducing the task execution time and inter task Communication delay.
Performance assessment of the monitored water metering device within the range of permissible measurement error

Purpose: determination of the performance of the developed water metering device under the field conditions, depending on various conditions and operating modes of the open channel, to obtain the required parameters of the water flow in the range of permissible error. Materials and methods. The developed water metering device consists of instrumentation and a section of a hydraulic structure. The instrumentation includes a level sensor (hydrostatic pressure sensor) and an autonomous remote control unit, consisting of a modem controller, a battery, a GSM antenna. The section of a hydraulic structure is a fortified section of an open channel that meets the requirements for measuring water discharge using the “velocity – area” method when calibrating and verifying measuring instruments. Results. At the first stage of testing, as a result of determining the level-flow characteristic in the control section of the channel, the equation Q = f(h) = 0.4961h² + 1.7319h – 0.4687 with a relative error of 2.67 % was derived, which was used for further calibration of the water metering device. At the second stage, testing of instrumental equipment was carried out with the determining the value of the relative measurement error, which was 2.23 %. As a result, the total relative measurement error of the monitored water metering device was 4.90 %, which does not exceed 5% and meets the requirements for similar measuring instruments. Conclusions. Based on the results of the studies, an algorithm has been developed for assessing the performance and determining the measurement error for new tested water metering devices, consisting of instrumental equipment (including a level sensor) and a hydraulic structure (open channel) that meets the requirements for measuring water discharge by the “velocity – area” method.