Reviewerly/exHarmony huggingface dataset:
Contrast-enhanced spectral mammography:are kinetic patterns useful for differential diagnosis of enhanced lesions?

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy.A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed.Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant.Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.
Dampak Game Online terhadap Pembelajaran Siswa di Sekolah Dasar

In this digital era, it affects many aspects of life, especially developments related to online games. Starting from children, teenagers, and adults, almost everyone knows what an online game is. All of that certainly has a significant impact, especially for students still in the age of education and learning. This study aimed to determine the impact of online games on student learning at SDN 020 Langsat Hulu, Kuantan Singingi Regency. The method used in this research is the qualitative method. The research procedure is initial observation, problem formulation, research implementation, interviews and documentation, and data analysis. Data analysis using the concept of data analysis techniques Miles and Huberman. The results of this study are that online games have both positive and negative impacts. The negative impact is that children with high-intensity playing games impact lesson values ​​at school. In conclusion, online games in today's era have developed very quickly. Many elementary school children play online games themselves, so children become addicted to playing online games, making their children's learning disrupted because they play online games too often.
MULTIVARIATE STATISTICAL METHODS IN DETERMINING THE SPATIAL DISTRIBUTION OF CHEMICAL ELEMENTS IN SOIL FROM THE PRESPA REGION, NORTH MACEDONIA

In this work, the results of the study of the distribution of 18 elements (Ag, Al, Ba, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Sr, V, and Zn) in the soil of the Prespa Region, North Macedonia, are presented.For this purpose, a total of 68 soil samples were collected from 34 different locations (34 samples of topsoil and 34 samples of subsoil).After total digestion, the samples were analyzed by atomic emission spectroscopy -inductively coupled plasma (ICP-AES).Special attention was paid to the distribution of Cr, Fe, Mg, Ni, and V.It was found that the highest contents of these elements are found in the soil samples from some areas of the Baba and Bigla Mountains, which is due to the lithogenic origin, since in this area there are mainly Paleozoic shales and magmatic and volcanic rocks, and therefore the contents of copper, lead, iron, and zinc are increased compared to European values.By applying factor analysis, 3 geochemical associations were identified: Factor 1 (Ni-V-Fe-Cr-Cu-Zn-Mn-Mg); Factor 2 (Mg-Sr-Na-Ca-Al), and Factor 3 (Ba-Pb-K).From these studies, it can be concluded that the distribution of these samples follows the geology of the region, and their contents are increased in certain areas of lithogenic origin.Characteristic of the Prespa region is that the content of copper, lead, iron, and zinc is elevated compared to the European values, because in this region there are mainly Paleozoic shales, Mesozoic, and Paleozoic carbonates and plutonites.
Crptography based Lifi for Patient Privacy and Emergency Health Service Using IOT

Medical care is one such region, where WIFI is as yet not utilized as the electromagnetic waves influences patients with sicknesses like neurological problems, diseases and so forth. Accordingly, LIFI can be respected the following large thing, as it represents no gamble to patients and offers more advantages than WIFI, such as faster speeds and a larger spectrum. The only issue that hospitals have while exchanging data through it is ensuring confidentiality. The methodology proposed here leverages Secure Hash Algorithms to give maximum security as a solution to this challenge. The Secure Hash Algorithm is a bonus feature that is mostly utilised for authentication. IoT connects physical devices such as sensors and actuators to networks. The programming routines can be visualised from any location thanks to cloud storage. These algorithms can be employed in a variety of applications, including smart homes, digital technologies, and banking systems. This research presents a model that takes into account a human's heart rate, glucose level, and temperature. In the even to fan emergency, adjacent hospitals are alerted to the patient's condition, allowing them to provide timely and correct care. This will save you from having to go to the hospital. Temperature, blood pressure, heart rate, gas sensor, and fall detection are among the vital signs monitored by the system. An Arduino controller and a GSM900Amodule make up the system design. The monitored values can be supplied via mobile phones, and if an abnormal state is detected, the buzzer is activated, and the information is communicated to the concerned members via the mobile app.
Comparison of voluntary and forced digital leaps in higher education – Teachers’ experiences of the added value of using digital tools in teaching and learning

The study examines the benefits of digital tools in teaching and learning as experienced by university teachers in two different time periods: 1) during the controlled digital leap before the Covid-19 pandemic (2017-2019) and 2) during the emergency imposition of remote teaching in response to the lockdown aimed at containing the Covid-19 pandemic (2020). Teachers in different academic fields at a large multidisciplinary Finnish university (N1 = 303 and N2 = 265) responded to two open-ended questions as part of a broader questionnaire. The research identified four kinds of benefits related to digital teaching: (1) practical and administrative and (2) independence of time and place, implying practical and technical benefits; as well as (3) enhancing learning and (4) developing teaching, which are primarily pedagogical benefits, related to teaching and learning. Voluntary and forced digital leaps provided different kinds of consequences in teaching and learning. Digital tools generally provided practical and technological benefits for teaching and learning during the controlled digital leap, but they also had positive pedagogical effects. The forced digital leap, in turn, demonstrated the value of time-and-place-independent online teaching and learning. It also exposed differences among academic fields in how teachers experienced the benefits of using digital tools. Support of and training for university teachers should take into account the different needs of faculties and disciplines, and promote dialogue between pedagogical and technological interests.
The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.
Face Detection and Recognition using Machine Learning Techniques

Face recognition of persons has received so much attention in the recent years due to its many applications in different fields such as security applications, video surveillance, biometric systems, identifying the criminals etc. This paper develops a system that can recognize the human face in the input image after it has been detected. The system is trained with set of faces and non faces, and when the input picture is given, the face is detected using Viola Jones Algorithm. In face recognition, the features are extracted from the training dataset using Principal Component Analysis (PCA) and then the system is trained to recognize the face using Support Vector Machine (SVM) classification. When the input image is given for face recognition, features are extracted from the input picture using PCA and multiclass classification is done by SVM.
Towards Immersive Generosity: The Need for a Novel Framework to Explore Large Audiovisual Archives through Embodied Experiences in Immersive Environments

Abstract This paper proposes an innovative framework to explore large audiovisual archives using Immersive Environments to place users inside a dataset and create an embodied experience. It starts by outlining the need for such a novel interface to meet the needs of archival scholars and the GLAM sector, and discusses issues in the current modes of access, mostly restrained to traditional information retrieval systems based on metadata. The paper presents the concept of “generous interfaces” as a preliminary approach to address these issues, and argues some of the key reasons why employing Immersive Visual Storytelling might benefit such frameworks. The theory of embodiment is leveraged to justify this claim, showing how a more embodied understanding of a collection can result in a stronger engagement for the public. By placing users as actors in the experience rather than mere spectators, the emergence of narrative is driven by their interactions, with benefits in terms of engagement with the public and understanding of the cultural component. The framework we propose is applied to two existing installations to analyze them in-depth and critique them, highlighting the key directions to pursue for further development.
Driver Identification Using Deep Generative Model With Limited Data

The scarcity of driving data constrains the accuracy of deep learning (DL)-based driver identification methods in practical application scenarios. To address this issue, this study proposes a novel unsupervised deep generative model called the convolution condition variant autoencoder (CCVAE) for driving data augmentation. In CCVAE, aided by driver identification information, the condition variant autoencoder can learn the real driving data distribution of each driver through an unsupervised learning paradigm; and aiming for better feature representation ability, convolutional neural network and deconvolution are leveraged, respectively. Therefore, a large number of synthetic samples can be generated by the generative part of the CCVAE. We demonstrate the effectiveness of the CCVAE through extensive experimental analysis using a real dataset collected from a vehicular CAN bus; the improvement of the DL-based driver identification results is demonstrated using synthetic samples. For instance, when only using 2% of the original data, approximately 20% improvement is achieved in terms of four evaluation indicators for two commonly used DL-based driver identification methods, namely, 1-D CNN and LSTM. Furthermore, several comparable experiments with state-of-the-art deep generative methods reveal the superior performance of the proposed CCVAE with respect to identification results, synthetic data quality, and model computation time. Therefore, the proposed model accomplishes a breakthrough in driver identification with limited data and shows great potential in data-driven applications of intelligent vehicles.
Click-through rate prediction model integrating user interest and multi-head attention mechanism

Abstract The purpose of click-through rate (CTR) prediction is to anticipate how likely a person is to click on an advertisement or item. It's required for a lot of internet applications, such online advertising and recommendation systems. The previous click-through rate estimation approach suffered from the following two flaws. On the one hand, input characteristics (such as user id, user age, user age, item id, item category) are usually sparse and multidimensional, making them effective. High-level combination characteristics are used for prediction. Obtaining it manually by domain experts takes a long time and is difficult to finish; also, customer interests are not all the same. The accuracy of the model findings will significantly increase if this immediately recognized component is incorporated in the prediction model. As a consequence, this study creates an IARM (interactive attention rate estimation model) that incorporates user interest as well as a multi-head self-attention mechanism. The deep learning network is used in the model to determine the user's interest expression based on user attributes. The multi-head self-attention mechanism with residual network is then employed to get feature interaction, which enhances the degree of effect of significant characteristics on the estimation result as well as its accuracy. The IARM model outperforms other recent prediction models in the assessment metrics AUC and LOSS, and it has superior accuracy, according to the results from the public experimental data set.
Sentiment Analysis Public Twitter on 2024 Election using the Long Short Term Memory Model

As a Democratic State in 2024 is a political year for Indonesia because there will be simultaneous elections starting from officials from the regions to the president, therefore it causes a lot of diverse reactions on social media, especially Twitter which can give rise to various kinds of opinions, not only positive but also positive opinions. also negative ones so that it becomes an interesting thing to study, the tendency of Twitter users to post content can be known through sentiment analysis, so the purpose of this study is to find out the sentiment of twitter users towards the 2024 election is negative or positive, the steps in this study begin with data collection tweets, preprocessing text mining and then entering lexicon based feature labels, building a model with Long Short Term Memory (LSTM) and evaluating the model with the confusion matrix tool used in this study using google collaboratory, sentiment analysis is carried out with gan 858 data from the preprocessing process with a comparison of 80% of train data and 20% of test data resulted in an accuracy of 78% with positive sentiment amounting to 52.2%, sentiment that was included in the positive mostly contained support for the success of the election, as well as information related to the election, and 37% negative sentiment which is included in the negative sentiment in this study include tweets with dirty words, misguiding opinions, and making fun of political and neutral parties by 10.8%. Keywords: 2024 Election, Sentiment Analysis, twitter, LSTM
Enhancement of quadripartite quantum correlation via phase-sensitive cascaded four-wave mixing process

Multipartite quantum correlation is important for both fundamental science and quantum information processing. Its enhancement is critical to improve the performance of physical systems. Therefore, it is of great practical significance to study the enhancement of multipartite quantum correlation. Here, we theoretically investigate a scheme for enhancing quadripartite quantum correlation by utilizing a phase-sensitive cascaded four-wave mixing (CFWM) process. We find that the intensity-difference squeezing (IDS) among the four output beams generated by the phase-insensitive CFWM process can be largely enhanced by introducing the phase-sensitive CFWM process. We also find that our phase-sensitive CFWM process can be used to generate intensity-sum squeezing (ISS), which cannot be generated by the phase-insensitive CFWM process. Moreover, we find that the maximum squeezing value of ISS is equal to that of IDS when the intensity gains of the three four-wave mixing processes are equal in the phase-sensitive cascaded scheme. In the end, we discuss the effects of the losses and the phase fluctuations on the squeezing values of our phase-sensitive CFWM process and the corresponding phase-insensitive CFWM process with the same intensity gains. It can be seen that the squeezing enhancement of our phase-sensitive CFWM process holds also in presence of the losses and the phase fluctuations. Our results pave the way for experimental implementation and may find applications in quantum metrology and quantum communication.
Developing Communicative Skills of Specialists of the Agro-Industrial Complex by Means of Information Technologies

This paper analyses the effectiveness of modern information technologies application in foreign language teaching for preparation of specialists of the agro-industrial complex. This article shows a great variety of ICT usage in higher education. One of the advantages of their application in language teaching is that these technologies contribute to developing speaking, listening, writing and lexical skills of students. Another benefit of digital resources implementation is that they motivate trainees in learning a foreign language independently. The conducted research gives the detailed analysis of different e-learning tools and proves that information technologies help to form communicative competence of learners. The communicative competence is one of the main as it supposes to use a language correctly in a professional sphere with the purpose of solving special professional tasks including the agro-industrial complex. The analysis of digital means is interesting and relevant for educational system and can be taken into account by teachers of foreign languages.
QoS prediction in intelligent edge computing based on feature learning

Abstract With the development of 5G and 6G, more computing and network resources on edge nodes are deployed close to the terminal. Meanwhile, the number of smart devices and intelligent services has grown significantly, which makes it difficult for users to choose a suitable service. The rich contextual information plays an important role in the prediction of service quality. In this paper, we propose a quality of service(QoS) prediction approach based on feature learning, the contextual information represented as the explicit features and underlying relationship hidden in the implicit features are fully considered. Then, the multi-head self-attention mechanism is used in the interacting layer to determine which features should be combined to form meaningful high-order features interaction. We have implemented our proposed approach with experiments based on real-world datasets. Experimental results show that our approach achieved a better performance of service QoS prediction in an intelligent edge computing environment for future communication.
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient?

For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives. While in the few-shot setup we observe that transformer-based object detectors perform better than convolution-based two-stage models for a similar amount of parameters, they are not as effective when used with recent approaches in the semi-supervised setting. In this paper, we propose a semi-supervised method tailored for the current state-of-the-art object detector Deformable DETR in the few-annotation learning setup using a student-teacher architecture, which avoids relying on a sensitive post-processing of the pseudo-labels generated by the teacher model. We evaluate our method on the semi-supervised object detection benchmarks COCO and Pascal VOC, and it outperforms previous methods, especially when annotations are scarce. We believe that our contributions open new possibilities to adapt similar object detection methods in this setup as well.