Reviewerly/exHarmony huggingface dataset:
Impact of CISO Appointment Announcements on the Market Value of Firms

Previous studies concerning the economic impact of security events on publicly listed companies have focussed on the negative effect of data breaches and cyberattacks with a view to encouraging firms to improve their cyber security posture to avoid such incidents. This paper is an initial study on the impact of investment in human capital related to security, specifically appointments of chief information security officers (CISO), chief security officers (CSO) or similar overall head of security roles. Using event study techniques, a dataset of 37 CISO type appointment announcements spanning multiple world markets between 2012 and 2019 was analysed and statistically significant (at the 5% level) positive cumulative abnormal returns (CAR) of around 0.8% on average were observed over the three-day period before, during and after the announcement. Furthermore, this positive CAR was found to be highest, at nearly 1.8% on average, within the financial services sector and showing statistical significance at the 1% level. In addition to the industry sector, other characteristics were investigated such as job title, reporting structure, comparison of internal versus external appointments, gender and variations between markets. Although these findings were not as conclusive they are, nevertheless, good pointers for future research in this area. This overall positive market reaction to CISO related announcements is a strong case for publicly listed firms to be transparent in such appointments and to, perhaps, review where that function sits within their organisation to ensure it delivers the greatest benefits. As 24% of the firms analysed were listed outside the US, this study also begins to counter the strong US bias seen in similar and related studies. This research is expected to be of interest to business management, cyber security practitioners, investors and shareholders as well as researchers in cyber security or related fields.
Identificação do perfil de estudantes de Medicina em relação ao uso excessivo do celular na sala de aula e a influência desta prática nas notas e no aprendizado dos estudantes / Identification of the profile of Medical students regarding the excessive use of cell phones in the classroom and the influence of this practice on students' grades and learning

influence of this practice on students' grades and learning
Repulsion Distance Intersection over Union Loss for Crowd Scene Detection

The detection of many people or crowds increases the false-positive prediction rate owing to the creation of more bounding boxes. In this study, a loss function that reduces the false positive predictions of an object detection model is proposed. The proposed loss function induces not only the bounding box to be closer to the ground truth (GT) box but also the center point distance between the GT box and the overlapped adjacent object or other bounding boxes to be longer. In addition, a balanced feature pyramid was introduced to enhance the precision of object prediction. Applying the proposed method, 𝑡ℎ𝑒 log-average miss rate on false positive per image in [10<SUP>−2</SUP>, 100] was 1.89% lower while average precision and Jaccard index were 0.5% and 0.58% higher, respectively, than those of the conventional method, which indicates that it effectively reduces false positive predictions.
An open science argument against closed metrics

In the Open Scientist Handbook, I argue that open science supports anti-rivalrous science collaborations where most metrics are of little, or of negative value. I would like to share some of these arguments here.
Bullying, Cyberbullying and Hate Speech

This paper applies the concepts of moral and social responsibility to the Internet in considering bullying and cyberbullying that result in loss of life. Specifically, I probe the moral and social responsibilities of Internet users (agents), of their immediate surroundings, and of the education system in fighting cyberbullying. Balance needs to be struck between two most important principles: freedom of expression and social responsibility. Illustrative examples in which this disturbing and harmful phenomenon of cyberbullying had cost young life are mentioned. It is argued that cyberbullying has some of the characteristics of hate speech and that many of the tools used to fight against hate may be utilised to fight against cyberbullying. It is further argued that all relevant stakeholders need to think of the consequences of their conduct, that Internet abusers should be accountable for their wrongdoing and be penalized, and that people who have the ability to stop or at least reduce the risk of cyberbullying should take proactive steps, exhibiting zero tolerance to cyberbullying. The article calls for a responsible concerted effort by responsible users of the Internet, parents, schools, governments, NGOs, and administrators of social networking sites to raise awareness of the problem, address it, and fight together to lessen and possibly eliminate it from the online information highway. Disregard for the consequences of both cyberbullying and hate speech and inactivity facing them are immoral.
PENGARUH PENGEMBANGAN SUMBER DAYA MANUSIA DAN PENGELOLAAN DANA BOS TERHADAP MUTU PENDIDIKAN SDN SE-KECAMATAN MAJALENGKA

This research is motivated by the phenomenon of the low quality of education in State Elementary Schools in Majalengka District, marked by the non-fulfillment of all applicable educational standards, aiming to determine the development of human resources; management of BOS funds and the quality of education in public elementary schools; and the influence of boss fund management and human resource development on the quality of education. The study used a quantitative approach with survey methods, data collection techniques using a questionnaire with a Likert scale distributed to 238 respondents. Processing and analyzing data using a parametric statistical approach, analyzed descriptively and verification. The results showed; Human resources and Boss Fund have been well managed and developed; Simultaneously the development of human resources and management of BOS funds have a significant effect on the quality of education with a magnitude of 74.70% and the remaining 25.30% influenced by other factors not examined. The conclusion, either partially or jointly, is that the development of human resources and the management of BOS funds affect the quality of education in Public Elementary Schools in Majalengka District
The Use of Integrating SQ3R Technique and Quizizz Application to Improve Students’ Reading Achievement

The present study assessed and compared two diﬀerent techniques for improving students reading achievement. Integrating SQ3R and Quiziz was compared to conventional SQ3R. Thirty one students of second grade SMA IT SMART INSANI participated in this study as experimental and control class. The instrument used in this study was reading test. The data of the research were collected through pre-test and post-test. The result showed that the integration of SQ3R and Quizizz in experimental class and conventional SQ3R in control class improved all the aspects of reading. The highest improve occurred in the aspects of identifying specific information and determining inference in experimental class and determining main idea aspect in control class. In general, the integration of SQ3R and Quizizz in experimental class gives better improvement in students reading achievement than conventional SQ3R. However, it was found that the significance level (2-tailed) was 0.126 higher than 0.05 that indicates there was no significant difference of students’ reading achievement between experimental class and control class. Based on the result of the study, it can be concluded that the implementation of Integrating SQ3R and Quizizz was better than conventional SQ3R to improve students reading achievement. The use of integration SQ3R and Quizizz significantly promoted students reading achievement. Keywords: SQ3R technique, Quizizz application, reading achievement.
Mapping TB Case Notification Rate (CNR) by Province in Indonesia

Background: Tuberculosis (TB) is still one of main public health problems in Indonesia.To efficiently help communities prevent TB, health offices need to be able to target TB prevention and intervention in populations with high risk.Case notification rate (CNR) has been used to estimate TB prevalence in community.This study aims to map provincial-level TB CNR and social determinants using publicly available secondary data to identify Indonesia provinces with high risk of TB to be targeted for focused TB prevention and interventions.Methods: Provincial-level TB CNR and its determinants such as poverty level, the percent of children having complete immunization, percent of village with Universal Child Immunization (UCI), population density, ratio of primary health care per subdistrict were collected using publicly available secondary data.GIS map were created to highlight areas with high risk of TB using ArcGIS.Statistical analysis was also performed using spearman correlation to confirm GIS findings. Results:The study indicated that there was variation of TB CNR across provinces in Indonesia that is correlated with percent of completed childhood vaccination, ratio primary healthcare per 100,000 population and percent of village with universal child immunization. Conclusion:Utilizing publicly available secondary data and GIS tools, the study identified variation of TB CNR at the provincial level in Indonesia for targeted TB prevention programs.
Blockchain Technique-Based Smart Health Record System

MedRecord focuses on storing medical records of patients in a decentralized network in order to provide security and privacy to the data of the patient. Smart contract is used to provide conflict-free service without the help of a third party. Smart contract is created in a platform called Ethereum. Hence, by using blockchain, we can safekeep and secure the medical reports of a patient.
ESTRATÉGIAS DE CUIDADO EM SAÚDE REALIZADAS PELAS EQUIPES DE CONSULTÓRIO NA RUA/BOMPAR/SMS. JUNTO À POPULAÇÃO EM SITUAÇÃO DE RUA DURANTE A PANDEMIA NO MUNICÍPIO DE SÃO PAULO

This research is part of the practical activity of Interconsultation carried out in the years 2015 and 2016 at Hospital Universitário Pedro Ernesto, teaching hospital of the Rio de Janeiro State University -UERJ.Interconsultation consists of an activity that, following the request for a technical opinion by the team, after apprehending some subjective question from the patient or family, the psychology professional is required to be monitored.Thus, we received the demand for GJ, 75 years old, diagnosed with Cutaneous T-Cell Lymphoma.In the service, we noticed a strong anguish in the health team, which signalized that the listening request was not restricted to the patient who, at that moment, was itself out of therapeutic care.The psychologist inside the general hospital is responsible for listening carefully to each case, creating space and opening possibilities for the speeches of the patient, family and team to emerge.In this study, a cut was made in this triad, emphasizing in the team.The research approach is characterized by highlighting the listening of the mental health professional in a multidisciplinary team as a differential in the treatment.A qualitative research was carried out, choosing the observation and analytical listening of the health team.The medical Psychology treatment offer and the unique look of Psychoanalysis operate as a contribution to the hospital context and to the health field.
A Hybrid Fuzzy Logic and Deep Learning Method for Fraud Detection: Application to Credit Cards

The credit card has become an essential part of business operations, both nationally and globally. It plays a very important role in today's economy. While the use of credit cards offers considerable benefits when used responsibly and with care, considerable financial and credit damage can be caused by fraudulent default. Many techniques have been proposed to combat the growth of credit card defaults. However, all these techniques have the same goal of preventing credit card defaults; each has its own characteristics, advantages, and disadvantages. In this paper, we applied two data mining techniques: Artificial Neural Networks and Fuzzy C-Means to this problem, we propose a hybridization between these two models and also, we show significant results of the three methods on real financial data. Therefore, out of all the methods used, good performances are obtained by using our hybrid model to fraud detection.
Online HealthCare System

Abstract: Today`s technology offers many online services in almost every field. From those fields Healthcare is one of them. However, in this pandemic it is very difficult to obtain the consultation with the doctor for every health problem so to overcome this problem the Online Healthcare System can be implemented. This Web application contains features like manage patient details, doctor`s detail, schedule appointments, view reports, online payment and some additional features like videos for “yoga and exercise” and a “Chatbot”, Which is a type of software that used to redirect the conversation between human beings and users which will help people to provide details according to their queries in a productive way.
Klasifikasi Penyakit Hipertensi dan Diabetes Berbasis Web Pada Klinik Pratama Rumkitban 01.08.03 Batam

The management of outpatient medical records at the Rumkitban Primary Clinic 01.08.03 Batam is still manual and causes many limitations and problems. This problem resulted in the inability of the clinic to run the Chronic Disease Treatment Program (PROLANIS) organized by BPJS-Health. The purpose of the study is to facilitate data processing and then from that data it can be used to classify hypertension and diabetes then the results of the classification are displayed in graphical form. This study discusses 2 diseases, namely hypertension and diabetes. The system uses the C45 Tree Decision Algorithm for automatic data processing. The attributes used are glucose, diastolic, systolic, insulin, and age to support the decision-making system. The system can make a decision whether the patient has hypertension, diabetes or not. The results of this study are the accuracy of classification accuracy in the system for hypertension shows 16.667% accuracy and 83.333% accuracy is not correct, then the calculation of diabetes classification accuracy shows 96.667% accuracy, and 3.333% accuracy is incorrect. This system is integrated with Mysql database to store the results.
Measuring Quality of Belief Function Approximations

Because of the high computational complexity of the respective procedures, the application of belief-function theory to problems of practice is possible only when the considered belief functions are approximated in an efficient way. Not all measures of similarity/dissimilarity are felicitous to measure the quality of such approximations. The paper presents results from a pilot study that tries to detect the divergences suitable for this purpose.
High-throughput screening with deep learning for quantitative phenotype analysis of zebrafish

Zebrafish is a useful biological model for analyzing genetic modification and large-scale screening. Its morphological evaluation, carrying meaningful information about genotype-phenotype relationship, is equally important. However, analysis of large amounts across development stages is a labor-intensive task. Here, we suggest a high-throughput monitoring technique using office scanner. Moreover, we developed deep learning models for extraction and analysis of massive statistical information. CNN-based architecture, forming the core of segmentation, serves as a basis for quantitative analysis and an early signal for embryo’s abnormal growth. Finally, compared to conventional microscope imaging, our scanning technique offers high-throughput, accurate, and fast quantitative phenotype analysis.
Optimizing hydrogen storage performance in ribbed annulus metal hydride reactors with lattice Boltzmann method and radial basis neural networks

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions.
Creations caused by humans (or robots)? Artificial intelligence and causation requirements for copyright protection in EU law

The current legal framework for protecting works of authorship in EU law presupposes that the work is the author's 'own intellectual creation'. The requirement is challenged in an environment where AI is used in creative processes. It is presumed that the concept of 'own intellectual creation' implies a causation requirement in that creation must be a result of the author's own intellectual efforts. The further content of the causation requirement has hitherto not been subject to much attention, but in an environment where both humans and machines influence the final result, the question about what elements in a work must be considered as caused by human endeavours comes to a head. Against that background this contribution discusses the further content of the implicit causation requirement in EU copyright law in the context of AI-generated works.
Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement

Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network (MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images. First, to avoid the high cost computation arising from the stacking of multiple sub-networks, our network uses a single encoder and decoder for multi-scale input and output images. Multi-scale input images can make up for the lack of pixel information and loss of feature map information caused by a single input image. The multi-scale output image can effectively monitor the error loss in the image reconstruction process. Second, the Convolutional Block Attention Module (CBAM) is introduced in the encoder part to effectively suppress the noise and color difference generated during feature extraction and further guide the network to refine the color features. Feature calibration module (FCM) is introduced in the decoder section to enhance the mapping expression between channels. Attention fusion module (AFM) is also added to capture contextual information, which is more conducive to recovering image detail information. Last, the cascade fusion module (CFM) is introduced to effectively combine the feature map information under different perceptual fields. Sufficient qualitative and quantitative experiments have been conducted on a variety of publicly available datasets, and the proposed MSFFNet outperforms other low-light enhancement methods in terms of visual effects and metric scores.
TableFormer: Table Structure Understanding with Transformers

Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a non-trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
Machine learning-based modeling of deep-sea polymetallic nodules spatial distribution: spatial autocorrelation and model transferability at local scales

&amp;lt;p&amp;gt;The spatial distribution of deep-sea polymetallic nodules (PMN) is of high interest due to increasing global demand in metals (Ni, Co, Cu), and their significant contribution to deep-sea ecology as hard-substrate. The spatial mapping is based on a combination of multibeam echosounders and underwater images in parallel to traditional ground-truth sampling by box coring. The combined analysis of such data has been advanced by using machine learning approaches, especially for automated image analyses and quantitative predictive mapping. However, the presence of spatial autocorrelation (SAC) in PMN distribution has not been extensively studied. While SAC could provide information regarding the patchy distribution of PMN and thus enlighten the variable selection before machine learning modeling, it could also result in an over-optimistic validation performance when not treated carefully. Here, we present a case study from a geomorphologically complex part of the Peru Basin. The local Moran&amp;amp;#8217;s I analysis revealed the presence of SAC of the PMN distribution, which can be linked with specific seafloor acoustic and geomorphological characteristics such as aspect and backscatter intensity. A quantile regression forests (QRF) model was developed using three cross-validations (CV) techniques: random-, spatial-, and feature space cluster-blocking. The results showed that spatial block cross-validation is the least unbiased method. Opposite the commonly used random-CV overestimates the true prediction error. QRF predicts well in morphologically similar areas, but the model uncertainty is high in areas with novel feature space conditions. Therefore, there is the need for dissimilarity analysis and transferability assessment even at local scales. Here, we used the recently proposed method &amp;amp;#8220;Area of Applicability&amp;amp;#8221; to map the geographical areas where feature space extrapolation occurs.&amp;lt;/p&amp;gt;