This study investigated the role of contextual factors in personnel selection. Specifically, I explored if specific job factors such as the wage, training, available applicant pool and security concerns around a job, influenced personnel decisions. Additionally, I explored if the individual differences of decision makers played a role in how the previously mentioned job factors affected their decisions. A policy-capturing methodology was employed to determine the weight participants place on the job factors when selecting candidates for different jobs. Regression and correlational analyses were computed with the beta weights obtained from individual regression analyses. The results obtained from the two samples (student and general population) revealed that specific job characteristics did indeed influence personnel decisions. Participants were more concerned with making mistakes and thus less likely to accept candidates when selecting candidates for jobs having high salary and/or high training requirements.

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Bakgrund I dagens programvara finns det problem som försämrar kvaliteten hos system och ökar kostnaderna. Det är viktigt att tänka på säkerheten redan under programmeringsfasen för att underlätta underhåll. The Open Web Application Security Project (OWASP) erbjuder dokument, verktyg och projekt för att skapa och underhålla produkter på ett säkrare sätt. För att upptäcka säkerhetsproblem i koden kan verktyg för Static Application Security Testing (SAST) användas. SAST-verktyg kan rapportera både false negatives och false positives, därför är det viktigt att undersöka hur precisa verktygen är i sin rapportering. Syfte Studien ämnar kartlägga vilka SAST-verktyg utvecklare kan ta hjälp av för att skriva säkrare kod. Undersökningen skall även jämföra hur bra de är på att hitta sårbarheter i kod och hur stort antal false positives de rapporterar. Metod En sökning gjordes för att samla information om vilka SAST-verktyg som finns tillgängliga och en lista sammanställdes med krav för att kunna genomföra likvärdiga tester. För att utföra testerna användes kod med planterade sårbarheter och resultaten från testerna genererade kvantitativa data som fördes in i en tabell. Resultat I studiens resultat kartlades tolv SAST-verktyg. Från dessa valdes HCL AppScan CodeSweep, Snyk och SonarLint ut för vidare testning. Därefter beräknades recall, precision och false positives för verktygen. Snyk hade 71,43% på både recall och precision och 33,33% false positives. HCL AppScan CodeSweep hade 28,57% på recall, 57,14% på precision och 25% på false positives. SonarLint hittade inga sårbarheter och blev därav inte analyserat. Slutsatser Studien kartlade tolv olika SAST-verktyg och valde tre för likvärdiga tester av JavaScript i Visual Studio Code. Resultaten visade att Snyk presterade bäst gällande rapportering av sårbarheter och hade högre resultat gällande precision, medan HCL AppScan CodeSweep presterade bäst på att undvika false positives. Överlag anses Snyk vara studiens bästa SAST-verktyg då det hade högst resultat på både recall och precision. / Background In today's software, there are issues that degrade system quality and increase costs. It is important to consider security during the programming phase to facilitate maintenance. The Open Web Application Security Project (OWASP) provides documentation, tools, and projects to create and maintain products in a more secure manner. To detect security issues in the code, tools for Static Application Security Testing (SAST) can be used. SAST-tools can report both false negatives and false positives, so it is important to investigate the accuracy of the tools in their reporting. Aim The study aims to map which SAST-tools developers can utilize to write more secure code. The investigation will also compare their effectiveness inidentifying vulnerabilities in code and the numberof false positives they report. Method A search was conducted to gather information on available SAST-tools, and a list was compiled with requirements to perform equivalent tests. To carry out the tests, code with planted vulnerabilities was used, and the test results generated quantitative data that were entered into a table. Results The study's results mapped twelve SAST-tools. From these, HCL AppScan CodeSweep, Snyk, and SonarLint were selected for further testing. Then, the recall, precision, and false positives were calculated for the tools. Snyk achieved 71.43% for both recall and precision and had 33.33% false positives. HCL AppScan CodeSweep achieved 28.57% recall, 57.14% precision, and 25% false positives. SonarLint did not find any vulnerabilities and was therefore not analyzed. Conclusions The study surveyed twelve different SAST-tools and selected three for tests on JavaScript in Visual Studio Code. The results showed that Snyk performed the best in terms of vulnerability reporting and achieved higher precision results, while HCL AppScan CodeSweep excelled in avoiding false positives. Overall, Snyk is considered the best SAST-tool in the study as it had the highest results in both recall and precision.

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This research is focused on identifying a single-step procedure that, upon adapting to the data through estimating the unknown parameters, would asymptotically control the False Discovery Rate when testing a large number of hypotheses simultaneously, and exploring some of the characteristics of this procedure. / Statistics

Yes / Aims: Levels of false positive referral to ophthalmology departments can be high. This study aimed to evaluate commonality between false positive referrals in order to find the factors which may influence referral accuracy. Methods: In 2007/08, a sample of 431 new Ophthalmology referrals from the catchment area of Bradford Royal Infirmary were retrospectively analysed. Results: The proportion of false positive referrals generated by optometrists decreases with experience at a rate of 6.2% per year since registration (p < 0.0001). Community services which involved further investigation done by the optometrist before directly referring to the hospital were 2.7 times less likely to refer false positively than other referral formats (p = 0.007). Male optometrists were about half as likely to generate a false positive referral than females (OR = 0.51, p = 0.008) and as multiple/corporate practices in the Bradford area employ less experienced and more female staff, independent practices generate about half the number of false positive referrals (OR = 0.52, p = 0.005). Conclusions: Clinician experience has the greatest effect on referral accuracy although there is also a significant effect of gender with women tending to refer more false positives. This may be due to a different approach to patient care and possibly a greater sensitivity to litigation. The improved accuracy of community services (which often refer directly after further investigation) supports further growth of these schemes. / This study was funded by the University of Bradford.

Automated vehicles promise to be one of the most constructive inventions of late as they promote road safety, fuel efficiency, and reduced time road travel, by decreasing traffic congestion and workload on the driver. In a platoon (which is a method of grouping vehicles, which helps increase the capacity of roads by managing the distance between vehicles by using electrical and mechanical coupling) of such automated vehicles, as in automated highway systems (AHS), tracking of inter-vehicular spacing is one of the significant factors to be considered. Because of the close spacing, computer-controlled platoons with inter-vehicular communication, which is the concept of adaptive cruise control (ACC), become open to cyber security attacks. Cyber physical and cyber attacks on smart grid systems in the electricity market have been a focus of researchers, and much work has been done on that front. However, cyber physical (CP) attacks on autonomous vehicle platoons have not been examined. Thus this research entails the survey of a number of vehicle models used in different works pertaining to longitudinal vehicle motion and analysis of a special class of cyber physical attacks called false data injection (FDI) attacks on vehicle platoons moving with longitudinal motion. In this kind of attack, an attacker can exploit the configuration of any cyber physical system to launch such attacks to successfully introduce arbitrary errors into certain state variables so as to gain control over the system. So here, an n-vehicle platoon is considered and a linearized vehicle model is used as a testbed to study vehicle dynamics and control, after false information is fed into the system.