Reviewerly/exHarmony huggingface dataset:
Multi branches dilated CNN federated learning for transmission line fault diagnosis

Using convolutional neural network for transmission line fault diagnosis is an accurate and effective method, but it relies on a large amount of data with positive labels and is limited by kernel size that decides the receptive field. However, it is difficult to centralize data in reality, which causes low accuracy of the model. Federated learning has made great progress recently, and it is possible to train a model with high accuracy without centralizing data. In this paper, we propose a transmission line fault diagnosis method based on multi-branch convolutional neural network combined with federated learning. First, we design a novel three branches network with two dilated convolution kernels to increase the receptive field of the kernel. Then we integrate it into the federated learning framework to expand the amount of data used to train the model while preserving data security and privacy. The experimental results show that our method is feasible and can effectively improve the accuracy of the model, and provides a new idea for transmission line fault diagnosis.
SOSIALISASI PEMILIHAN DAN PENGGUNAAN KOSMETIK CREAM PEMUTIH WAJAH YANG AMAN PADA SANTRIWATI PESANTREN MODERN FAJRUL IMAN PATUMBAK DELI SERDANG SUMATERA UTARA

In this technological era, it is easy to get information about the facial whitening cream products, various information media such as television, newspapers, magazines and social media which are booming among women, especially school-age teenagers. With the ease of this information, buyers often ignore the quality and safety of the cosmetics themselves, many cosmetics contain harmful bleaching agents such as mercury and hydroquinone. The purpose of this Community Service is to find out the description of knowledge and socialize the Safe Selection and Use of Face Whitening Cream for students at the Fajrul Iman Patumbak Islamic Boarding School. The method used in this community service is lectures and questions and answers about the Selection and Use of Safe Face Whitening Cream Cosmetics. From the results of community service before the counseling was held on the Selection and Use of Safe Face Whitening Cream Cosmetics at Fajrul Iman Modern Islamic Boarding School students, it was found that the knowledge category was quite good (23.3%) in the good category (76.7%), and after the counseling was carried out the category was obtained. good knowledge becomes (100%), there is an increase in knowledge of good category as much as 23.3%. So that the Fajrul Iman Islamic boarding school students as respondents already understand the Selection and Use of Safe Face Whitening Creams
WalkBand - New Musical Instrument for Classroom Music Education

Due to a great many students and limited space, classroom music education is often achieved through collective teaching and students are usually passive recipients.Oneto-many teaching mode leads to little interaction or cooperation between students in class.This teaching mode cannot free students from making natural response to rhythm or music.It focuses on appreciation or solos and hardly involves creation.Indeed, there are problems in the basis of music education -musical instruments, that is, interface.Existing musical instruments are designed for performance instead of education.Moreover, instruments mainly used in Chinese music education for children feature the timbre of western musical instruments, thus obstructing the inheritance and transmission of Chinese music culture.
CATAN: Chart-aware temporal attention network for adverse outcome prediction

There is an increased adoption of electronic health record systems by a variety of hospitals and medical centers. This provides an opportunity to leverage automated computer systems in assisting healthcare workers. One of the least utilized but rich source of patient information is the unstructured clinical text. In this work, we develop CATAN, a chart-aware temporal attention network for learning patient representations from clinical notes. We introduce a novel representation where each note is considered a single unit, like a sentence, and composed of attention-weighted words. The notes in turn are aggregated into a patient representation using a second weighting unit, note attention. Unlike standard attention computations which focus only on the content of the note, we incorporate the chart-time for each note as a constraint for attention calculation. This allows our model to focus on notes closer to the prediction time. Using the MIMIC-III dataset, we empirically show that our patient representation and attention calculation achieves the best performance in comparison with various state-of-the-art baselines for one-year mortality prediction and 30-day hospital readmission. Moreover, the attention weights can be used to offer transparency into our model's predictions.
A Data-Centric Approach Towards Deducing Bias in Artificial Intelligence Systems for Textual Contexts

With the ever-growing usage of artificial intelligence in business decisions, organizations, and the day-to-day lives of the society at large, it is imperative that different forms of biases that human beings are privy to do not get trickled to the machine learning and artificial intelligence systems being developed and used in abundance.
Research of DBN PLSR algorithm Based on Sparse Constraint

DBN is a generative model based on unsupervised learning, with strong computing and information processing capabilities. But at the same time, there are some drawbacks: the model is constructed through intensive expression, which leads to relatively low computing performance of the network. The network optimization method based on the BP algorithm is easy to fall into a local minimum, which makes DBN fine-tuning accuracy is reduced. In order to obtain a DBN that is efficient and can avoid local optimization, the paper designs a DBN based on adaptive sparse representation and partial least square regression (PLSR) fine-tuning. First, two regularization factor terms are introduced to punish the densely expressed connection characteristics, thereby constructing an sparse RBM. Secondly, PLSR method is adopted instead of the BP algorithm, and a PLSR model is established between every two layers from the output layer to the input layer. The experiment proved the effectiveness of optimized DBN in improving network performance and learning performance.
Digital footprints in the university space

This work considers the use of digital traces in the educational environment and the specifics of their collection and analysis at the university. One way or another, all participants in the educational process, as well as those who can potentially become them, for example, applicants, leave digital traces in the digital environments of the university and the Global Network in general, and these traces can be analyzed. At the same time, even the university itself as an organization leaves a certain digital footprint on the Internet. At the moment, most researchers are very optimistic, contemplating on what positive changes can be brought by the analysis of digital traces of applicants, students and teachers for the development of the university itself, the educa-tional process, and the formation of individual learning paths. In contrast to this, the author identifies a number of possible prospects for the analysis of Big Data and the use of Artificial Intelligence for education at the university of the future. Attention is focused on how this can affect the safety of the environment and conflict with ethical standards. Participants in the educational process, falling under the analysis of their digital traces, can both suffer because of them, even if their activities have not been in any way connected with the university, and begin to hide their true digital identity, creating «false» digital traces and becoming anon-ymous. The author assumes that an increase in such control covering actions, thoughts and emotions naturally results in the emergence of the concept of a «Dark» University, which distances itself as much as possible from such methods of analyzing personal data.
A Disruptive Innovation of Blockchain in Finance and Banking: A Jump into the Future

This study presents a review of the literature on blockchain’s applications in the financial and banking sectors. It contributes to the existing literature identifying current research trends and practical implications for policy-makers and practitioners.
Criteria and indicators for determining the efficiency of the school management team

The article reflects aspects concerning the degree of applicability of the criteria and indicators in determining the efficiency of the school management team. The indicators classified on social, cultural, psychological development domains, of teamwork skills and managerial skills highlight ways of transformation of the management team, incentive in the creation of a macro team of the school organization. Through applying the grid it follows the tendency of the managerial cadres to self-improvement, self-formation and amplification of the chances for the team to become effective.
Multivariate density estimation from privatised data: universal consistency and minimax rates

We revisit the classical problem of nonparametric density estimation but impose local differential privacy constraints. Under such constraints, the original multivariate data X1,…,Xn∈Rd cannot be directly observed, and all estimators are functions of the randomised output of a suitable privacy mechanism. The statistician is free to choose the form of the privacy mechanism, and in this work we propose to add Laplace distributed noise to a discretisation of the location of an observed vector. Based on these randomised data, we propose a novel estimator of the density function, which can be viewed as a privatised version of the well-studied histogram density estimator. Our theoretical results include universal pointwise consistency and strong universal L1-consistency. In addition, a convergence rate for Lipschitz continuous functions is derived, which is complemented by a matching minimax lower bound. We illustrate the trade-off between data utility and privacy by means of a small simulation study.
About the new manual on scientific, methodological and science studies training of postgraduate and doctoral students

This publication is a review of the manual by I.V. Ponkin and A.I. Redkina «Methodology of scientific research and applied analytics» (Moscow: Buki Vedi, 2020. — 365 p.). The author reveals the content of the manual, its structure, features of the presentation of the text, and the language and speaks about the evidence of this work and the thoughtfulness of its methodological apparatus. At the same time, the article notes that this manual differs significantly from the existing set of educational literature, which largely accounts for its originality. English version of the article is available at URL: https://panor.ru/articles/discussing-the-new-textbook-on-the-scientific-methodological-and-science-study-training-of-postgraduates-and-doctoral-students/70478.html
FAIRness dos planos de gestão de dados: uma avaliação de alguns PGDs europeus

The FAIR principles have become a data management instrument for the academic and scientific community, since they provide a set of guiding principles to bring findability, accessibility, interoperability and reusability to data and metadata stewardship. Since their official publication in 2016 by Scientific Data – Nature, these principles have received worldwide recognition and have been quickly endorsed and adopted as a cornerstone of data stewardship and research policy. However, when put into practice, they occasionally result in organisational, legal and technological challenges that can lead to doubts and uncertainty as to whether the effort of implementing them is worthwhile. Soon after their publication, the European Commission and other funding agencies started to require that project proposals include a Data Management Plan (DMP) based on the FAIR principles. This paper reports on the adherence of DMPs to the FAIR principles, critically evaluating ten European DMP templates. We observed that the current FAIRness of most of these DMPs is only partly satisfactory, in that they address data best practices, findability, accessibility and sometimes preservation, but pay much less attention to metadata and interoperability.
Multi-Scale Cascade Guided Object Detection in Aerial Images

Object detection in aerial images has received increasing attention during the last few years. Scale variation is one of the main challenges in large scene aerial images. Existing object detection pipelines usually detect objects of different scale objects at multiple scale layers. However, the conventional detection approaches with multi-scale density predictions could cause duplicate detections of the same object. In this paper, we proposed a multi-scale cascade guided detection framework (MCGNet) to address these issues by guiding the different scales in detector focus on different scale objects. In particular, we proposed a multi-scale cascade module to predict the different scale objects with an explicit constraint in the loss function. Experiments on benchmark DOTA show promising performance of MCGNet compared with other detectors. Code will be released at https://github.com/jason-su/MCGNET.
On Characterization of Transparency Order for (n, m)-functions

The transparency order (denoted by \(\mathcal {TO}\)) is a useful measure of the robustness of (n, m)-functions (cryptographic S-boxes as mappings from \(GF(2)^n\) to \(GF(2)^m\)) to multi-bits Differential Power Analysis (DPA). An improved version of transparency order (denoted by \(\mathcal {RTO}\)), based on the use of cross-correlation coefficients, was also introduced recently. For the first time, we resolve this open problem which (n, m)-functions reach the upper bound on \(\mathcal {TO}\) for odd n (m is a power of 2). We also investigate the tightness of upper and lower bounds related to \(\mathcal {RTO}\) and derive its relationship to main cryptographic characterizations of (n, m)-functions (such as nonlinearity, the sum-of-square indicator and algebraic immunity). Finally, concerning S-boxes of size \(4\times 4\), the distributions of \(\mathcal {RTO}\) for all 302 balanced S-boxes (up to affine equivalence) and 16 equivalence classes of optimal S-boxes are given.
Machine Learning-Based Model Selection for Anomalous Wireless Link Detection

Machine learning (ML) techniques play a significant role in detecting abnormal link operations in wireless networks. However, during the design and development process of a ML-based model, numerous factors including input data transformation, scaling methods, choice of ML techniques and their associated parametrization, influence the final model performance. In this paper, we discuss the design and development process of wireless link anomaly detection models followed by the model ranking process for selecting the most suitable models given the required application criteria. Under this premise, we consider four different data representations, four anomaly types, six scaling methods and six distinct ML techniques along with their associated parametrization trials leading to over twenty thousand anomaly detection models for wireless links. We show that the most suitable supervised ML models are able to detect anomalies with an F1 score of at least 88% and up to perfect score, whereas unsupervised ML models can attain an F1 score of up to 96% depending on the anomaly type, and on average, amongst 20,352 available models, the supervised SVM model can be readily adopted to efficiently detect any type of anomaly with an F1 score of at least 94%, particularly when trained on encoded data representations using the combination of "mean and deviation scaling" and "non-linear RBF kernel".
Taking Digital Product Design Coordination to the Next Level by Elastic Zooming and Linecept

extended-abstract Share on Taking Digital Product Design Coordination to the Next Level by Elastic Zooming and Linecept Author: David T Kutas Department of Informatics University of California, Irvine, United States Department of Informatics University of California, Irvine, United StatesView Profile Authors Info & Claims UIST '21: The Adjunct Publication of the 34th Annual ACM Symposium on User Interface Software and TechnologyOctober 2021 Pages 179–182https://doi.org/10.1145/3474349.3477592Online:10 October 2021Publication History 0citation41DownloadsMetricsTotal Citations0Total Downloads41Last 12 Months41Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Ethical Approaches to Studying Cybercrime: Considerations, Practice and Experience in the United Kingdom

Traditional normative ethics focuses on doing what is right as agreed by the community at large. For society as a whole, this is likely the protection of property and the safety of individuals, making the criminal a justifiable target of law enforcement and research. For criminals themselves, loyalty to fellow criminals may take precedence instead. Similarly, as research ethics introduces imperatives such as ensuring the rights and expectations of research participants are respected, informed consent and anonymity, it is unclear where these rights apply. A criminal in a research study is unlikely to give consent but expects anonymity. Yet have they intentionally waived such rights by committing crimes? In this chapter, we explore the ethical challenges related to research into cybercrime in the United Kingdom and how this relates to normative approaches, including a critical evaluation of guidance from British social science professional associations. We conclude with a set of recommendations to researchers of cybercrime.
Cell Detection by Robust Self-Trained Networks

Cell nuclear detection on digital histopathology images plays an important role on computer-assisted cancer diagnostics. However, lack of manual annotations and variability of cells bring great challenges to fully-supervised learning. Therefore, we propose a Robust Self-Trained Network (RSTN) for cell detection. The backbone is an encoder-decoder trained by distance maps (DMs) generated from dot annotations of nuclei. To save manual efforts, RSTN is designed to involve reliable predicted DMs in optimization and detect cell centers for unknown images automatically. RSTN gains robustness by regularizing the network by dynamic graphs of DM patches. It exploits underlying graph structures and recognizes complex spatial patterns to locate cells of various shapes and colors. Experimental results show that it outperforms several classic and advanced models on both simulated fluorescence microscope images and real pathology slides for cell detection.
An Empirical Study on the Countermeasures of Implementing 5G Multimedia Network Technology in College Education

Aiming at the problem of 5G multimedia heterogeneous multimodal network representation learning, this paper proposes a collaborative multimodal heterogeneous network representation learning method based on attention mechanism. This method learns different representations for nodes based on heterogeneous network structure information and multimodal content and designs an attention mechanism to learn weights for different representations to fuse them to obtain robust node representations. Combining the general process of exploring the college physical education model and the characteristics of the multimedia network classroom environment, this article constructs the process of exploring the college physical education teaching model of the multimedia network classroom. Through the research and practice of the inquiry college physical education teaching model in the multimedia network classroom, it is verified that the implementation of the inquiry college physical education teaching in the multimedia network classroom can effectively influence and increase the students’ interest in learning and stimulate the students’ inner learning motivation. Through the guidance and training of teachers, a variety of disciplines can be used to carry out college physical education in multimedia network classrooms, so that the integration between courses can be truly realized, with the aim that all courses can share the excellent results brought by the development of modern education technology. More educators understand, accept, and participate in the practice of college physical education based on multimedia network classrooms and better serve the education of college physical education. The construction of the college physical education evaluation system should be combined with the characteristics of the 5G multimedia network era. The evaluation process includes data collection, data analysis, result output, and result feedback. Each link is an indispensable part of the college physical education evaluation process. Based on the relevant knowledge of the 5G multimedia network, the evaluation indicators determined in this study can basically reflect the various elements of the physical education process in colleges and universities. The distribution of index weight coefficients is more scientific and reasonable. Compared with the current system, the college physical education evaluation system constructed by exploration has a certain degree of objectivity and scientificity. Therefore, it is feasible to apply the 5G multimedia network to the evaluation of college physical education.
Bunch-of-Keys Module for Optimizing a Single Image Detector Based on the Property of Sequential Images

In image processing, deep learning networks have been continuously developed and are used in many fields. However, most networks do not reflect image continuity. In this paper, we propose a novel bunch-of-keys module connected to the backend of a deep learning network to improve the detector performance on sequential images. This module optimizes existing deep learning networks to detect sequential images without retraining. This procedure reduces time and computing costs, and the average precision improves with a minimal drop in the frames per second. By adopting a sliding window method that uses three consecutive images, the keys are generated by comparing the positions of the detected boxes for each of the images using generalized intersection over union. The two key types perform correcting operations. The rectifying key has the effect of adding or merging undetected bounding boxes in mid-frame. The tracking key has the effect of compensating for bounding boxes lost for no reason in the third frame. The candidate box extracted using each key determines whether to add or merge to the target image in the correction task. This task calculates the complete intersection over union (CIoU) score between candidate boxes and all boxes of the target image and is divided into add or merge cases according to a set of CIoU criterion. As a result of adding or merging the bounding box to the missing object, detection performance was improved up to 3% in terms of the average precision.