1,720,972 research outputs found
The Impact of Stress on Healthcare Staffs’ Cybersecurity Practices
Full text linkDigitalization has revolutionized the healthcare industry, offering numerous advantages. However, it has also introduced the risk of data breaches through cyber-attacks. Healthcare information systems, containing valuable data that can be sold at high prices, are often targeted by adversaries. Surprisingly, a recent report revealed that 82% of data leaks involved a human element. As a result, the study of human behavior in cybersecurity has gained significant attention. However, the relationship between stress levels and cybersecurity practices, particularly in the healthcare setting, has only been the subject of a small number of peer-reviewed studies. This study aims to fill this gap by examining the relationship between stress levels and risky cybersecurity practices among hospital workers. Additionally, it investigates how stress impacts email judgment performance. Furthermore, the study compares different strategies to develop effective multimodal stress detection systems. To achieve these objectives, the research methodology employs correlation analysis, causal analysis utilizing a randomized controlled trial (RCT), and comparative analysis of various machine learning models.
The correlation analysis reveals a positive correlation between stress levels and risky cybersecurity practices in Ghana, Indonesia, and the combined dataset from three countries (Ghana, Norway, and Indonesia), indicating that individuals experiencing higher stress are more likely to engage in behaviors compromising cybersecurity. The causal analysis shows that while stress does not directly compromise participants’ ability to detect phishing emails, higher stress levels are significantly correlated with lower accuracy in the Indonesian context. Furthermore, completion time is identified as a potential mediator of the impact of stress on email judgment performance, with longer time associated with better performance. While the result from Norway showed no significant difference, the result from Indonesia suggested that participants in the non-stress group took a significantly longer time to complete judging emails than participants in the stress group.
The comparative analysis of multimodal stress detection systems demonstrates the superiority of multiple sensor fusion models over individual sensors with the weighted score-level fusion approach getting the best performance. Furthermore, preprocessing such as feature normalization and feature selection was proven to improve system performance. In term of classifier, using Logistic Regression as the classifier yield the best results.
This study contributes to our understanding of the impact of stress on cybersecurity practices and emails judgment performance. These findings have important implications for hospital management, emphasizing the need for targeted training programs and support systems to enhance cybersecurity practices among staff. The comparative analysis provides insights into effective multimodal stress detection systems, promoting privacy and accuracy. In conclusion, It offers practical recommendations for healthcare organizations to enhance cybersecurity and provides insights for the development of effective multimodal stress detection systems
Determining the age and gender of an individual based on text classification - Comparing two binary classifications with one 4-class classification
Full text linkAlder og kjønndeteksjon er en av verktøyene som kan brukes for å sørge for en form for sikkerhet i chatterom. Ved å finne riktig aldersgruppe på en bruker ved hjelp av teksten den har skrevet, kan denne studien beskytte unge barn, både fra å utgi seg som unge voksne på nettet, og fra overgripere som utgir seg for å være barn. Denne studien vil forsøke å forbedre deteksjon av alder og kjønn ved tekstklassifisering ved å finne forkjeller mellom å se på alder og kjønnklassifisering som to separate binære problemer, og et 4-klasse klassifiseringsproblem.
Ved å bruke seks forskjellige algoritmer, tre forskjellige måter å hente attributter på, og implementering av to forskjellige måter å behandle resultatene, for både binær og 4-klasse-klassifisering, sørger studien for et solid grunnlag for sammenligning. Beregningene som er valgt til å brukes i sammenligningen er accuracy, precision, recall, databehandlingstid, i tillegg til F_0.5 og F_1 score. Fokuset vil ligge på precision og F_0.5 score, ettersom det er et potensiale for å bruke dette til å detektere overgripere, vil det være mer relevant å detektere voksne som utgir seg for å være barn. Dette er basert på at klassifiseringen for de binære metodene klassifiserer barn som 1 og voksne som 0. Resultatene fra 4-klasse-klassifisering blir også kombinert til to deler, en for alder og en for kjønn, slik at resultatene blir sammenlignbare.
Mellomliggende resultater viser at hard voting har en større påvirkning på resultatene enn soft voting. Dette gjelder både for binær- og kombinert 4-klasse-klassifiseringer, men mest for 4-klasse-klassifiseringer.
Resultatene viser at databehandlingstiden til 4-klasse-klassifisering er markant raskere enn for to binære klassifiseringer, ettersom de må kjøres to ganger. Forskjellene vedrørende de andre beregningene varierer mellom de forskjellige metodene, fra omtrent ingen forskjell til 60%, hvor de største forskjellene skjer ved de metodene som samlet har dårligst resultater, på kjønnklassifisering med hard voting. Forskjellene i gjennomsnittlig precision og F_0.5 score er 1.6% og 4% henholdsvis, til fordel for kombinert data 4-klasse-klassifisering. Ved å se på spesifikke brukere, og om klassifiseringen med binære og kombinert data 4-klasse-klassifisering er forskjellig, så klassifiserer sistnevnte 4.3% flere brukere korrekt.
Forskjellene mellom de forskjellige methodene er ikke alltid signifikant, men fra et overordnet standpunkt klassifiserer kombinert data 4-klasse-klassifisering med bedre resultater i 70.8% av metodene brukt i denne studien, med tanke på precision og F_0.5scores. Dette tyder på at denne tilnærmelsen kan være et bedre valg med tanke på alder og kjønnsdeteksjon ved tekstklassifisering i for eksempel chatterom.Age and gender detection is one of the tools that can be used to provide a form of safety in chatrooms. By finding the correct age group of an author of a chat, or text, this study could protect young children, either from posing as young adults online themselves or from predators seeking them out, pretending to be children. This study seeks to improve the detection of age and gender through text classification by finding the differences between looking at age and gender classification as two separate binary problems, or as one 4-class classification problem.
By running six different algorithms, three different feature extraction methods, and implementing soft and hard voting on the results, from both the binary classifications and 4-class classifications, it provides a solid basis for comparison. The metrics chosen as comparative numbers are accuracy, precision, recall, computing time, as well as F_0.5 and F_1 scores. The focus is on precision and the F_0.5 score because, given the potential application in detecting predators, it is more relevant to detect adults posing as children. This is given that the classifications for the binary methods are based on a child being class 1, and an adult being class 0. The results from the 4-class classification are also combined into two parts, one for age and one for gender, in order to have more comparable results.
Intermediate results show that hard voting has a more substantial effect on the results than soft voting. It does so for both the binary and the 4-class combined data, but mostly for the 4-class classifications.
The results show that the computing time for the 4-class classification is by far the faster choice, as the classification for the binary data must be run twice. The differences with regards to the other metrics vary between the different methods and range from negligible to 60%, where the highest differences occur for the worst performing methods overall, on gender classification and hard voting. The difference in average precision and F_0.5 score is 1.6% and 4% respectively, in favor of the 4-class combined data classification. Looking at specific authors, and if the classification differed between binary and 4-class combined classification, the latter classifies 4.3% more authors correctly.
The difference between the different methods is not always significant, but from an overall standpoint, the 4-class combined data classifications perform better in 70.8% of the methods used in this study, with regards to precision and F_0.5 scores. This suggests that this approach could be the better choice in detecting age and gender through text classification in e.g., chatrooms
Determining the age and gender of an individual based on text classification - Comparing two binary classifications with one 4-class classification
No full textAlder og kjønndeteksjon er en av verktøyene som kan brukes for å sørge for en form for sikkerhet i chatterom. Ved å finne riktig aldersgruppe på en bruker ved hjelp av teksten den har skrevet, kan denne studien beskytte unge barn, både fra å utgi seg som unge voksne på nettet, og fra overgripere som utgir seg for å være barn. Denne studien vil forsøke å forbedre deteksjon av alder og kjønn ved tekstklassifisering ved å finne forkjeller mellom å se på alder og kjønnklassifisering som to separate binære problemer, og et 4-klasse klassifiseringsproblem.
Ved å bruke seks forskjellige algoritmer, tre forskjellige måter å hente attributter på, og implementering av to forskjellige måter å behandle resultatene, for både binær og 4-klasse-klassifisering, sørger studien for et solid grunnlag for sammenligning. Beregningene som er valgt til å brukes i sammenligningen er accuracy, precision, recall, databehandlingstid, i tillegg til F_0.5 og F_1 score. Fokuset vil ligge på precision og F_0.5 score, ettersom det er et potensiale for å bruke dette til å detektere overgripere, vil det være mer relevant å detektere voksne som utgir seg for å være barn. Dette er basert på at klassifiseringen for de binære metodene klassifiserer barn som 1 og voksne som 0. Resultatene fra 4-klasse-klassifisering blir også kombinert til to deler, en for alder og en for kjønn, slik at resultatene blir sammenlignbare.
Mellomliggende resultater viser at hard voting har en større påvirkning på resultatene enn soft voting. Dette gjelder både for binær- og kombinert 4-klasse-klassifiseringer, men mest for 4-klasse-klassifiseringer.
Resultatene viser at databehandlingstiden til 4-klasse-klassifisering er markant raskere enn for to binære klassifiseringer, ettersom de må kjøres to ganger. Forskjellene vedrørende de andre beregningene varierer mellom de forskjellige metodene, fra omtrent ingen forskjell til 60%, hvor de største forskjellene skjer ved de metodene som samlet har dårligst resultater, på kjønnklassifisering med hard voting. Forskjellene i gjennomsnittlig precision og F_0.5 score er 1.6% og 4% henholdsvis, til fordel for kombinert data 4-klasse-klassifisering. Ved å se på spesifikke brukere, og om klassifiseringen med binære og kombinert data 4-klasse-klassifisering er forskjellig, så klassifiserer sistnevnte 4.3% flere brukere korrekt.
Forskjellene mellom de forskjellige methodene er ikke alltid signifikant, men fra et overordnet standpunkt klassifiserer kombinert data 4-klasse-klassifisering med bedre resultater i 70.8% av metodene brukt i denne studien, med tanke på precision og F_0.5scores. Dette tyder på at denne tilnærmelsen kan være et bedre valg med tanke på alder og kjønnsdeteksjon ved tekstklassifisering i for eksempel chatterom
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
