Reviewerly/exHarmony huggingface dataset:
Defending Adversarial Patches via Joint Region Localizing and Inpainting

Deep neural networks are successfully used in various applications, but show their vulnerability to adversarial examples. With the development of adversarial patches, the feasibility of attacks in physical scenes increases, and the defenses against patch attacks are urgently needed. However, defending such adversarial patch attacks is still an unsolved problem. In this paper, we analyse the properties of adversarial patches, and find that: on the one hand, adversarial patches will lead to the appearance or contextual inconsistency in the target objects; on the other hand, the patch region will show abnormal changes on the high-level feature maps of the objects extracted by a backbone network. Considering the above two points, we propose a novel defense method based on a ``localizing and inpainting" mechanism to pre-process the input examples. Specifically, we design an unified framework, where the ``localizing" sub-network utilizes a two-branch structure to represent the above two aspects to accurately detect the adversarial patch region in the image. For the ``inpainting" sub-network, it utilizes the surrounding contextual cues to recover the original content covered by the adversarial patch. The quality of inpainted images is also evaluated by measuring the appearance consistency and the effects of adversarial attacks. These two sub-networks are then jointly trained via an iterative optimization manner. In this way, the ``localizing" and ``inpainting" modules can interact closely with each other, and thus learn a better solution. A series of experiments versus traffic sign classification and detection tasks are conducted to defend against various adversarial patch attacks.
Neural-based Cross-modal Search and Retrieval of Artwork

Creating an intelligent search and retrieval system for artwork images, particularly paintings, is crucial for documenting cultural heritage, fostering wider public engagement, and advancing artistic analysis and interpretation. Visual-Semantic Embedding (VSE) networks are deep learning models used for information retrieval, which learn joint representations of textual and visual data, enabling 1) cross-modal search and retrieval tasks, such as image-to-text and text-to-image retrieval; and 2) relation-focused retrieval to capture entity relationships and provide more contextually relevant search results. Although VSE networks have played a significant role in cross-modal information retrieval, their application to painting datasets, such as ArtUK, remains unexplored. This paper introduces BoonArt, a VSE-based cross-modal search engine that allows users to search for images using textual queries, and to obtain textual descriptions along with the corresponding images when using image queries. The performance of BoonArt was evaluated using the ArtUK dataset. Experimental evaluations revealed that BoonArt achieved 97% Recall@10 for image-to-text retrieval, and 97.4% Recall@10 for text-to-image Retrieval. By bridging the gap between textual and visual modalities, BoonArt provides a much-improved search performance compared to traditional search engines, such as the one provided by the ArtUK website. BoonArt can be utilised to work with other artwork datasets.
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023

Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems. One of the research directions aimed at improving the position of tabular DL involves designing so-called retrieval-augmented models. For a target object, such models retrieve other objects (e.g. the nearest neighbors) from the available training data and use their features and labels to make a better prediction. In this work, we present TabR -- essentially, a feed-forward network with a custom k-Nearest-Neighbors-like component in the middle. On a set of public benchmarks with datasets up to several million objects, TabR marks a big step forward for tabular DL: it demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed "GBDT-friendly" benchmark (see Figure 1). Among the important findings and technical details powering TabR, the main ones lie in the attention-like mechanism that is responsible for retrieving the nearest neighbors and extracting valuable signal from them. In addition to the much higher performance, TabR is simple and significantly more efficient compared to prior retrieval-based tabular DL models.
Smart System Empowerment Through Virtual Business Incubator to Increase Student Entrepreneurial Capacity in Higher Education

The purpose of this journal is to present the development and implementation of a virtual incubator aimed at assisting students in establishing their businesses.The virtual incubator offers the flexibility and accessibility required for entrepreneurial endeavors to thrive in an increasingly interconnected world.The research methodology employed a research and development approach, combining interviews with key personnel from university incubator departments, lecturers, and students, as well as conducting Focus Group Discussions (FGDs).The collected data informed the creation of a virtual incubator prototype, which was subsequently tested at the Nobel Indonesia Institute of Technology and Business.The virtual incubator serves as an assistant to student entrepreneurs, providing them with the necessary tools and resources to develop their business ideas Virtually.By utilizing information and communication technologies, the virtual incubator enables students to access mentorship, networking opportunities, and relevant resources regardless of their physical location.This eliminates geographical barriers and allows students to work on their businesses at their convenience, promoting a flexible and conducive entrepreneurial environment.The research findings demonstrate the successful implementation of the virtual incubator prototype, showcasing its effectiveness in supporting student entrepreneurs.Through interviews and FGDs, insights were gained into the preferences and requirements of students, as well as the challenges they face when establishing businesses.These findings guided the development of a user-friendly and comprehensive virtual incubator platform.
Does Banking Sector Advancement Provide a Growth-Supporting Role to Tanzania Economy?

This paper aimed at examining the role of banking sector development on economic growth in Tanzania.To explore the long-run relationship between financial growth and economic development a log-linear equation with auto-regressive term, accounting for serial correlation, is estimated after conducting a regression diagnostic test.The results show that financial depth positively influence economic growth, and this technically shows that the financial depth accelerates economic growth through expanding access to those who do not have adequate access to finance.The paper recommends the Government to have a policy of subsidizing access to finance to enable informal businesses, which do not have easy access to finance, to boost the growth of their businesses.Furthermore, the policymakers are encouraged to embrace an efficient banking system, and more specifically, Central Bank of Tanzania must spearhead the policy of lowering banking lending rates so as to promote investment which will ultimately foster economic growth.
Görselleştirme Okuryazarlığı Çalışmalarında Değerlendirmenin Karşılaştırmalı Bir İncelemesi

Data visualization is a powerful tool that simplifies complex datasets into easily comprehensible visual representations, making it easier to identify patterns and relationships within the data. To fully understand data visualization, individuals must develop visual literacy skills, which entails effectively understanding, interpreting, and creating visualizations. Assessing visualization literacy skills through literacy tests is crucial, and educational tools play a significant role in advancing these skills by providing guidance and resources. However, determining the suitable settings for data collection in literacy tests and evaluating educational tools is a complex and demanding task. This paper presents a comparative analysis based on two literacy studies introducing literacy tests and evaluating the effectiveness of pedagogical tools for Treemap and Parallel Coordinates Plot (PCP) through classroom and crowdsourcing experiments. The analysis focuses on key factors, including data collection, study time and resources, data quality, data validity, and challenges encountered during experiments. The findings underscore the significance of considering specific research questions and target populations when selecting experimental settings for visualization literacy and educational purposes.
Information extraction from weakly structured radiological reports with natural language queries

Abstract Objectives Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models. Methods We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports. Results Continued pre-training of a BERT model on the radiology dataset and a medical online encyclopedia resulted in the most accurate model with an F1-score of 83.97% and an exact match score of 71.63% for answerable questions and 96.01% accuracy in detecting unanswerable questions. Fine-tuning a non-medical model without further pre-training led to the lowest-performing model. The final model proved stable against variation in the formulations of questions and in dealing with questions on topics excluded from the training set. Conclusions General domain BERT models further pre-trained on radiological data achieve high accuracy in answering questions on radiology reports. We propose to integrate our approach into the workflow of medical practitioners and researchers to extract information from radiology reports. Clinical relevance statement By reducing the need for manual searches of radiology reports, radiologists’ resources are freed up, which indirectly benefits patients. Key Points • BERT models pre-trained on general domain datasets and radiology reports achieve high accuracy (83.97% F1-score) on question-answering for radiology reports. • The best performing model achieves an F1-score of 83.97% for answerable questions and 96.01% accuracy for questions without an answer. • Additional radiology-specific pretraining of all investigated BERT models improves their performance. Graphical Abstract
Pentingnya Penerapan Literasi Digital dalam Meningkatkan Kreativitas Peserta Didik di Sekolah Dasar

The emergence of digital literacy today is due to the development of information and communication technology which is not only printed, but already in digital form. Digital literacy is a form of skill that refers not only to the abillty to use technology, information and communication tools, but also social skills, learning skills, attitudes, critical thinking, creativity and inspiration such as digital skills. Digital literacy can help someone think critically, solve problems and communicate fluently. In addition, digital literacy can provide opportunities to work with people. Digital literacy encourages students to be creative or participate in learning activities freely, creatively and innovatively.
HÁBITOS DE VIDA, NÍVEL DE ATIVIDADE FÍSICA E QUALIDADE DE VIDA DE UNIVERSITÁRIOS DO SUDOESTE GOIANO DURANTE A PANDEMIA DO CORONAVÍRUS

Descriptive study that sought to analyze the influence of social isolation (SI) on habits, physical activity level (PAL) and quality of life (QoL) of the university population. 258 academics from the Federal University of Jataí (UFJ), Goiás, Brazil participated. For data collection, sociodemographic questions, lifestyle habits, presence of diseases and anthropometric data were sent via email to Google Forms®. The International Physical Activity Questionnaire – IPAQ-Short, and the Europe Health Interview Surveys Quality of Life-8 were used for the NAF, data were analyzed using descriptive and inferential statistics, with p&lt;0.05. The participation of women predominated ( n=177; 68.6%). There was a prevalence of anxiety in females (p = 0.001). In QOL, the female population had a poor/regular perception of satisfaction (p=0.01) and willingness (p=0.001) for activities of daily living, and satisfaction with oneself (p=0.04). There is an association between anxiety and low PA level, and a lower QoL classification when compared to non-anxious individuals. There was a negative correlation between QOL and age (r= -0.14; p=0.02) and between QOL and body mass (r= -0.15; p = 0.01). university student submitted to IS showed anxiety and a reduction in quality of life indicators, negatively impacting health.Keywords: Physical Inactivity; Anxiety; Life Style.
Trascendiendo la educación binaria: pensamiento disruptivo en la era conceptual

The review article characterizes the theoretical debate between two ways of understanding the scientific process and the educational conceptions that emerge from them. Using sociocritical methodology, the sociohistorical development of a binary paradigm in the structuring of the scientific method and the need to transcend it are explored. The research results emphasize the fundamental role of a curriculum focused on the liberal arts to promote the foundation of critical and innovative knowledge from the disruptive as a necessary complement in the hyper-technological world of the 4.0 revolution.
Camera calibration approach using polarized light based on high-frequency component variance weighted entropy for pose measurement

Abstract The camera’s intrinsic parameters calibration directly affects monocular pose measurement accuracy. To improve the calibration accuracy of the camera’s intrinsic parameters, the polarizing device is used to eliminate the high-brightness areas in the images collected for camera calibration. However, an unsuitable polarizing angle will reduce the image’s contrast. This causes the texture information of the image to be lost or makes the extracted feature change in the direction of grayscale value decline, ultimately affecting the feature extraction. Aiming at this issue, this paper proposed a camera calibration approach using polarized light based on high-frequency component variance weighted entropy (HVE). We determined the suitable polarizing angle according to the two-dimensional homography matrix describing the mapping between the target plane and normalized imaging plane for each calibration location. Under the suitable polarizing angle, we determined the optimal exposure time according to the HVE. The images collected under suitable polarizing angles and the optimal exposure time were used to finish the camera calibration. The suitable polarizing angles and optimal exposure time can ensure the image’s contrast and texture information. The experimental findings demonstrate that our approach can increase the camera calibration accuracy and thus pose measurement accuracy. In the −45° to +45° range, the average pose measurement error is less than 0.05° and lowered by 0.08° (62%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device. In the 0–20 mm range, the average pose measurement error is less than 0.028 mm and lowered by 0.05 mm (64%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device.
Research on privacy protection based on network traffic data

This paper discusses the research of privacy protection based on network traffic data. Firstly, the concept and collection method of network traffic data are introduced, and the method of collecting network traffic data by intelligent identification algorithm is proposed, so as to improve the accuracy and efficiency of data collection. Then, aiming at the privacy protection of network traffic data, a privacy protection scheme based on desensitization algorithm is proposed. The scheme uses chaotic cryptography, homomorphic encryption and differential privacy to ensure the privacy and security of data. Finally, the effectiveness and practicability of the scheme are illustrated by the conclusion, which provides a new solution for the privacy protection of network traffic data.
IMPROVING INDONESIAN LEARNING OUTCOMES USING THE AUDIOLINGUA METHOD IN ELEMENTARY SCHOOL STUDENTS

Learning outcomes can be interpreted as learning consequences in the form of information, mastery, or skills, and attitudes acquired by students during academic lessons and expressed in numbers. The purpose of this study was to find out how the Application of Fun Learning Methods in Improving Student Learning Outcomes in Class IV Indonesian Language at Wameo 2 Public Elementary School. This research was conducted through the process of planning, implementing, observing and evaluating. The subjects in this study were 15 grade 3 students of School Students Country 2 Wameo The results of this study indicate that the pre-cycle has not shown learning outcomes, after using the Audiolingua method in Cycle I and Cycle II there is an increase in Indonesian learning outcomes using the Audiolingua method
The importance of Banking sector in the growth of Nation economy: A case study of Stanbic bank in South Sudan

The banking industry is essential to supporting other institutions and general economic development in sub-Saharan African countries, acting as a vital support system for other institutions and overall economic development. This research aims to examine the correlation between banking sector assets, bank deposits, and liquid liabilities and their implications for the economy. A comprehensive survey was conducted, targeting all commercial banks in South Sudan. The questionnaire was completed by 50 directors, managers, and senior officers. The findings showed a tenuous inverse link between consumer deposits and economic expansion. To address these concerns, it is imperative for the government to establish a stable political system, exercise fiscal discipline, and establish effective information infrastructure within the banking sector. Policymakers should also prioritize tackling tax evasion and avoidance to bolster bank profitability and effectively deal with the challenges faced by the economy.
The Impact of Spatial Resolution on Optimal Renewable Energy Portfolios

Efforts in the energy planning department are required to achieve the target levels of renewable energy penetration and electricity demand in the European Union.Mean-variance analysis is commonly used to identify the optimal deployment of variable renewable energy sources.By using it, we can determine the most effective ways to increase renewable energy penetration and minimise supply risks through varying spatial and technological deployments.In this study, we investigate the effectiveness of optimising capacities at the scale of climate data grid points, rather than administrative regions, which is a common approach due to data availability and computation costs.We find that a finer description of climate resources and variable renewable energy capacity factors results in a better exploitation of complementarities and offers increased degrees of freedom in optimisation.Our analysis reveals that better describing local conditions results in two advantages over lower resolution approaches: higher average capacity factors and generation combinations with lower covariances.Additionally, we find that higher resolution approaches significantly reduce variability in daily and annual climate frequencies in renewable generation under the optimal scenario.These results highlight the importance of accounting for detailed climate information when identifying optimal renewable deployment scenarios and can provide valuable support for stakeholders and policy makers in making sustainable commitments.
The Effectiveness of the Green Design and Manufacturing Decision in Achieving the Outstanding Performance of the Industrial Companies: An Analytical Study in Al-Zawraa General Company

The objective of this research is to analyze the impact of product design decisions and green manufacturing on Al-Zawraa General Company's competitive advantage in Iraq. Data was collected through a questionnaire that measured key variables، such as product design، green manufacturing، and outstanding performance. Each major variable was further evaluated through three sub-dimensions. Out of the 75 employees invited to participate، 60 staff responded to the questionnaire. Quantitative and qualitative methods، such as arithmetic means، standard deviations، and confirmatory factor analysis، were employed to examine the effect of the key variables in implementing the green manufacturing system in the company. The study's findings suggest that the company's management should reconsider its approach to green technology by implementing modern automation methods that utilize environmentally friendly energy sources. This change would not only help to reduce industrial pollution rates and benefit the environment but also enhance the company's appeal to consumers and ensure its longevity and competitiveness in the local and global industrial markets. Furthermore، the study revealed a strong correlation between the key variables and the successful adoption of the green manufacturing system.
The Danger of Predator Journal Publication for Researchers

Scientific publications in the form of scientific journals are one way for researchers to share information related to the results of research that has been done, besides that scientific publications can also be used to build the reputation of researchers both in the academic and research fields. However, there is a danger of publication which until now has not been known by novice researchers who have just entered the world of scientific publications, namely publication in predatory journals. This is an important thing that needs to be known well and also must be watched out for by novice researchers. This is because predatory journals are scientific journals that are not of good quality, where the journals that are managed are only for financial gain through expensive publication fees and do not pay attention to aspects of research quality and also a good peer review process in good governance. scientific journals. In researching and writing this journal, researchers discuss some of the dangers associated with publishing predatory journals for researchers in terms of decreasing academic reputation, publication costs and also falsification of research data published in a predatory journal. In addition, researchers also look at the specific and clear characteristics of journals that can be categorized as predatory journals.
Education in Times of Pandemic a New Normal from Teaching Practice

To explore the repercussions of virtual education from teaching practice. The approach used is the qualitative systematic bibliographic review; consulting academic literature. There are numerous coincidences that teachers faced in the context of the health emergency, the need for self-training, lack of digital skills and emotional conditions. The new normal conditions a more digital education trend, it is time for a new transition where technology must be incorporated into the Peruvian school as a necessary tool for the learning process.