Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF AUGMENTED REALITY IN MOTORCYCLES INTRODUCTION LEARNING

Augmented reality technology in the field of education can be used as an interactive learning media that can minimize boredom during the conventional teaching and learning process, namely by using books containing text and pictures.The advantages of augmented reality technology, it can be used to explain basic motorcycle introduction materials, which usually require a complete explanation not only from writing and pictures. Therefore, this study aims to create an interactive learning media for motorcycle engine introduction based on augmented reality. The method used in this learning media is Research and Development consisting of needs analysis, design design, design implementation, testing, expert validation, media revision, feasibility test, media improvement, and product. The results of this study are interactive learning media applications that display 3-dimensional objects regarding motorcycle engines using audio explanations for each of the existing 3-dimensional objects, as well as adding a quiz feature to increase interactivity in the teaching and learning process.
COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION

Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.
Analysis of Relationships in the Virtual Space of Social Networks

The article is devoted to a brief review of approaches to the analysis of social relations in social networks using comments and credentials located in the profiles of social network users. The study is aimed at revealing the behavior of the user and understanding the nature of his relationship with others. The proposed architectural scheme of the system for the intellectual analysis of social relations will make it possible to better understand and identify social relations between users of social networks.
Analysis of flood control in Lambidaro sub-watershed using EPA SWMM

In the Lambidaro sub-watershed, precisely on Jalan Colonel Sulaiman Amin, the problem of flooding continues, especially when it rains. The occurrence of flooding around the canal along the Colonel Sulaiman Amin road drainage is caused by several factors, one of which is the capacity of the reservoir which is not able to control the flood discharge optimally so that water cannot overflow into the canal. and eventually caused flooding in the surrounding area. This study aims to determine whether the drainage capacity is still able to drain surface runoff water or not and determine alternative flood control solutions for Colonel Sulaiman Amin Street. The method used is the EPA Model SWMM (Environmental Protection Agency Storm Water Management Model). From this study, the simulation results obtained from EPA SWMM showed alternative solutions offered such as the use of flood pumps, retention ponds, and optimization of drainage, the most effective solution of the six flood mitigation experiments was channel optimization in the form of changing the shape and size of the channel 7 to a square channel with a width of 1 .5 meters. This alternative is 5.26% more effective with an effective percentage of 85.538% and can drain 36.36% more water flow than drainage with a widening of 1.1 meters.
Fine-Grained Interaction Modeling with Multi-Relational Transformer for Knowledge Tracing

Knowledge tracing, the goal of which is predicting students’ future performance given their past question response sequences to trace their knowledge states, is pivotal for computer-aided education and intelligent tutoring systems. Although many technical efforts have been devoted to modeling students based on their question-response sequences, fine-grained interaction modeling between question-response pairs within each sequence is underexplored. This causes question-response representations less contextualized and further limits student modeling. To address this issue, we first conduct a data analysis and reveal the existence of complex cross effects between different question-response pairs within a sequence. Consequently, we propose MRT-KT, a multi-relational transformer for knowledge tracing, to enable fine-grained interaction modeling between question-response pairs. It introduces a novel relation encoding scheme based on knowledge concepts and student performance. Comprehensive experimental results show that MRT-KT outperforms state-of-the-art knowledge tracing methods on four widely-used datasets, validating the effectiveness of considering fine-grained interaction for knowledge tracing.
Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.
Wavelet Clustering Based on Cluster Signal

Wavelet transform can capture the detailed information of signals and discover the fluctuation in the signals. The intention of wavelet clustering is to introduce the wavelet transform to analyze cluster signal and partition the dataset into clusters. Thus, the key is to construct the cluster signal. In this paper, a new wavelet clustering is proposed based on cluster signal. The cluster signal refers to cluster order, which is constructed by neighborhood propagation with the constraint of local density. Cluster order implies the density fluctuation of clusters and wavelet transform can identify the partitioned index in the cluster order and accomplise clustering. Comprehensive experiments show that the proposed wavelet clustering achieve better performance of clustering than compared methods.
A Chaos-Based and Ensembled Method for Outlier Detection

With the advent of the Big Data era, anomaly detection has become an important tool for screening the validity of data. Many well-established distance-based or correlation-based anomaly detection methods are widely used for various structured and feature-based datasets with the increasing size of data. However, different method strategies have different focuses, leading to large deviations in anomaly detection results for the same dataset using different methods, which poses a great challenge to anomaly detection research. In this paper, a new strategy is proposed for anomaly detection using integrated methods. By using a two-stage process of the sliding window aggregation method, the strategy uses a multi-model anomaly scoring method and a uniform quantitative criterion filtering to obtain a suitable anomaly scoring.
On randomized sketching algorithms and the Tracy–Widom law

Abstract There is an increasing body of work exploring the integration of random projection into algorithms for numerical linear algebra. The primary motivation is to reduce the overall computational cost of processing large datasets. A suitably chosen random projection can be used to embed the original dataset in a lower-dimensional space such that key properties of the original dataset are retained. These algorithms are often referred to as sketching algorithms, as the projected dataset can be used as a compressed representation of the full dataset. We show that random matrix theory, in particular the Tracy–Widom law, is useful for describing the operating characteristics of sketching algorithms in the tall-data regime when the sample size n is much greater than the number of variables d . Asymptotic large sample results are of particular interest as this is the regime where sketching is most useful for data compression. In particular, we develop asymptotic approximations for the success rate in generating random subspace embeddings and the convergence probability of iterative sketching algorithms. We test a number of sketching algorithms on real large high-dimensional datasets and find that the asymptotic expressions give accurate predictions of the empirical performance.
Adaptive Decay and Radius-Based Evolving Clustering Approach for Data Stream

The application of data clustering in data analysis field has been increased dramatically. These data clustering techniques are convenient for the field of data mining. For clustering data stream, arbitrary shaped density based clustering approach is proficient to produce clusters. Most of the existing data clustering approaches are hybrid online or offline approach. These approaches are able to find only hyper elliptical clusters. Recently two fully online clustering algorithms called CEDAS and BOCEDS were proposed which is able to handle the property of data stream that evolves over time. In these existing online clustering techniques, a permanent decay variable which is specified and not update over time. This decay variable is predetermined when it is initialized. It can also be an erroneous choice like global optimal radius. In this paper, we represent an online based evolving clustering approach based on adaptive decay and radius. In this approach, a graph structure is preserved where nodes are used to represent the micro-clusters and edges are used to represent the pairs of intersecting micro-clusters. These cluster graphs are evolved over time. Furthermore, adaptive decay and adaptive radius are considered in order to avoid the arbitrary choice of these parameters. The experimental result of this algorithm is compared with the existing methods and it outperforms.
Supervised Learning

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
InterCLIP: Adapting CLIP To Interactive Image Retrieval with Triplet Similarity

Interactive image retrieval is such task setting where a multi-modal query (reference image, feedback text) is provided, and the goal is to retrieve a target image which satisfies the changes described in feedback text based on the reference image. It offers a great promise for better user experience in a variety of fields such as e-commerce where the user can address their need with natural language and find the desired item iteratively. With the rising of Vision-Language Pre-trained(VLP) models, it has become a de facto to transfer rich knowledge learned from large-scale real-world data to downstream tasks. In this work, we propose a novel method called InterCLIP, which adapt the matching oriented VLP model CLIP, to the task. To further harness the power of CLIP, we propose to view the task as a combination of text-image retrieval and standard image search. Specifically we calculate candidate images’ similarity score with similarity within the triplet. This method allows fine-grained modelling which takes account of the relevance between three pairs within the triplet, and extensive experiments show our method achieves state-of-the-art results on the FashionIQ dataset.
Medical-Network (Med-Net): A Neural Network for Breast Cancer Segmentation in Ultrasound Image

Breast tumor segmentation is an important image processing technique for cancer diagnosis and treatment. Recently, deep learning models have shown significant advances toward computer-aided diagnosis systems (CAD). We proposed a novel neural network-based attention modules to segment tumors from breast ultrasound (BUS) images. Inspired by the human brain function of interpreting the scene, in this contribution, we focused only on the salient areas of the image, while suppressing other details. This was built on a residual encoder and dense blocks decoder. The generated feature map comprises spatial as well as channel details and fused the maps producing more meaningful feature map and gives better discriminative characteristics. The results show that the proposed model outperformed several recent models and has potential for clinical practices.
A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation

Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. There have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets.
Pose Sequence Model Using the Encoder-Decoder Structure for 3D Pose Estimation

Human pose estimation is a hot research problem in computer vision, it has a certain application prospect in the automatic driving industry, security field, film and television industry, and specific action monitoring of special scenes. Because a 2D skeleton usually corresponds to multiple 3D skeletons, the mapping from 2D to 3D in the monocular video has inherent depth ambiguity and is ill-posed, which makes the research on the technology of 3D human pose estimation in monocular video challenging. In this paper, a Pose Sequence Model (PSM) for 3D human pose estimation in the monocular video is proposed, which combines the full convolution neural network based on extended convolution with the Long Short-Term Memory (LSTM) network. We make full use of convolution to extract spatial features and use LSTM to obtain temporal features. With this model, we can predict 3D human posture through 2D sequences. Compared with the previous work on classical data sets, our method has good detection results.
AUTOMATE SENTIMENTAL ANALYIS OF TEXTUAL COMMENTS AND FEEDBACK

Now a days everything is Automated.We watch movies and most of us give the feedback about the movie and also, many people believe in the reviews given in the review websites like Twitter and so on.The reviews may be going towards good things or bad things, some people say good some say, bad but knowing overall true review is difficult.Some people may use irrelevant words, unwanted data also.This should not happen.Reading all the reviews is difficult, and finding the relevant words about the movie is also difficult.So, if we can make this easy to the audience (People) it would be very nice and it would be great thing.So, we collect the reviews preprocess it so that redundancies are removed and data becomes consistent.Then, vectorizing the content takes place so that model can easily process the data.Now, we start building the model and we split the dataset to train and test the model accuracy.To analyze the model results we will have the accuracy plot, we also try to make a function which takes text as an input and gives the sentiment of the review (positive or negative).At last, we can see the reviews sentiment using Data Visualization by Using Naïve Baye's classifier we have achieved 94% accuracy.
Design features of thermal flow sensors for biomedical purposes

It is shown that thermal flow sensors are characterized by high structural and technological compatibility with biomedical equipment and, compared to other types of flow sensors, they most fully meet the requirements for this equipment. The developed signal converters fully meet the requirements for modern biomedical equipment and energy-saving low-voltage electronics (unipolar 3V power supply), provide a negligible thermal effect on the flow environment and are implemented on the latest element base - high-precision CMOS railto-rail operational amplifiers, bidirectional multiplexers of the type ADG, ADuC type microconverters, powerful D-MOS HEX FET transistors, etc
ICT tools in Designing Preschool Educational Activities on Historical Events

Digital technology is advancing at a rapid pace today. During the Covid-19 pandemic, the forced transition to e-learning promoted non-traditional work practices and time-sensitive tools. In Greek universities, like in most parts of the world, the lockdown changed curricula and lecturers responded by modi-fying course designs. Different academic disciplines and specific topics began to be delivered in different ways using the non-traditional teaching approaches of microlearning. In this study, conducted during the 2021 spring semester, we re-viewed the digital material that undergraduate students studying to become teach-ers had prepared as part of their ICT course. One hundred seventy students com-pleted their teaching internship reports with the help of digital infrastructure and ICT tools. The students were asked to deliver a preschool lesson addressing the historically significant topic of the Greek Revolution of 1821. Participants used 90 different ICT tools, lying in 5 categories: web search engines, social media, game-based learning platforms, collaboration platforms and task-specific tools. These ICT tools had different functionality, as some helped to create digital ma-terial, some helped to edit it, while others served to transmit it through online communication channels. Digital material and ICT tools can enhance and support educational experiences by providing various pathways to overcome the possible disruptions caused by the Coronavirus Era.
Collab Pro –A Project Repository

Abstract: In today’s digital world, many college students learn new technology through digital medium and use this technology innovatively to create new projects/outcomes during their college curriculum. Unfortunately, these projects are not used effectively, or they don’t get any recognition. Many students dedicatedly learn new technology and use them very innovatively, many of such ideas have caliber of forming a successful startup. Many of thecurrent startups have been have succeeded which were started by youths. We studied the existing collaboration software and have proposed an android platform for university student to showcasetheir projects and get recognition. The application will also help other students to study the uploaded projects and suggest any reviews. The referee will have control to approve the projects uploaded by students to avoid plagiarism. The application will also help the company recruiters to find innovative projects and consider the author for further recruitment processes. The android application wasdeveloped using Java. The data of the application isstored/managed on Google Firebase. Android Studio served as IDE at the development stage. The project is dedicated for providing a dedicatedplatform for students to showcase their projects andget national recognition and provide a platform to recruiters for hiring talented authors.
Transformation of Scientific Objectivity and Scientific Self: Specificities of Ethical and Epistemological Regime of Digitalization

The development in recent decades of empirical ways to investigate and describe science generates a change in the conceptual apparatus of epistemology, i.e. the content and functions of epistemic concepts, in particular, the concepts of ob­jectivity and self. Scientific objectivity ceases to be understood as an abstract epistemic concept in modern epistemology. It begins to refer to specific histori­cally varied practices, that a scientist must perform in order to be objective. The scientific self as a special kind of identity turns out to be historically prac­ticed along with the scientific objectivity. A certain type of scientific self devel­ops under the influence of certain scientific practices (in particular, practices of creating visual images) and practices of following a certain “epistemic virtue” (in particular, objectivity). There are, various visualization methods (use of dia­grams, maps, photographs, compilation of atlases) become one of the fundamen­tal and integral parts of building scientific argumentation in modern scientific practices. The purpose of our research is an attempt to explicate a possible an­swer to the question of what is the past, present and possible future of scientific objectivity and scientific self in the era of digitalization.