Cloud Forensics : Isolating Cloud Instance

Cloud computing has been the trending model for storing, accessing and modifying the data over the Internet in the recent years. Rising use of the cloud has generated a new concept related to the cloud which is cloud forensics. Cloud forensics can be defined as investigating for evidence over the cloud, so it can be viewed as a combination of both cloud computing and digital forensics. Many issues of applying forensics in the cloud have been addressed. Isolating the location of the incident has become an essential part of forensic process. This is done to ensure that evidence will not be modified or changed. Isolating an instant in the cloud computing has become even more challenging, due to the nature of the cloud environment. In the cloud, the same storage or virtual machine have been used by many users. Hence, the evidence is most likely will be overwritten and lost. The proposed solution in this paper is to isolate a cloud instance. This can be achieved by marking the instant that reside in the servers as "Under Investigation". To do so, cloud file system must be studied. One of the well-known file systems used in the cloud is Apache Hadoop Distributed File System (HDFS). Thus, in this paper the methodology used for isolating a cloud instance would be based on the HDFS architecture

The Effect of Training and Development Effectiveness on Jordanian Municipalities

Purpose: This study investigates the impact of training and development effectiveness on Jordanian municipality's performance. It aims to understand the relationship between training programs and municipal outcomes, with a focus on employee performance and organizational effectiveness.   Theoretical framework: The study employs a descriptive analysis approach to explore its objectives. It relies on a questionnaire-based data collection method, targeting all workers in Jordanian municipalities and the Greater Amman Municipality. The research uses statistical analysis software SPSS for data analysis and hypothesis testing.   Design, methodology, approach: The study's design involves surveying 7043 employees across various job titles in Jordanian municipalities and the Amman municipality. The questionnaire serves as the primary data collection tool. The research utilizes the SPSS program for statistical analysis and hypothesis validation. Descriptive statistics and analytical methods are employed to assess the effectiveness of training and development programs.   Findings: The study reveals that the majority of employees are male, aged 40 or younger, with less than 15 years of service. Around 78.5% of employees hold a diploma or higher qualification. The research indicates an average level of approval regarding the performance of municipalities and employees. It establishes a significant impact of training and development effectiveness on municipal and employee performance, influenced by factors such as academic qualification and age.   Research, practical & social implications: The findings emphasize the need for tailored training programs, especially for younger employees and newcomers. The study advocates for comprehensive training preparation, evaluation based on predefined criteria, and the allocation of financial resources in municipal budgets for ongoing training and development initiatives. These insights have practical implications for enhancing municipal employee performance and, consequently, the overall functioning of Jordanian municipalities.   Originality: This study contributes original insights by investigating the specific context of Jordanian municipalities. It sheds light on the effectiveness of training and development programs, highlighting their influence on employee and municipal performance. The research provides unique recommendations, emphasizing the importance of targeted training for specific employee groups and the necessity of financial allocation for sustained training efforts

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Linkage crossover operator for genetic algorithms

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will lead a genetic algorithm (GA) faster towards better results. In many problems, crucial knowledge is to be found not in individual components, but in interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generally chosen for problems. The strengths of linkages between components of a chromosomal structure can be explicitly represented in a linkage matrix and used in the reproduction step to generate new individuals. For some problems, such a linkage matrix is known a priori from the nature of the problem. In other cases, the linkage matrix may be learned by successive minor adaptations during the execution of the evolutionary algorithm. We present four linkage adaptation procedures that work along with the linkage based crossover. We demonstrate the success of such an approach for several benchmark problems representing two types of problems: One where the linkage structure is well-defined and is easily represented as linkage matrix in advance, and another where the linkage structure is not well-known. Linkage adaptation procedures show success in learning the right linkage structure for a given problem, and obtaining better results than the standard crossover operators. This approach is shown to be robust to different variations of GA parameter values

Práticas e desafios da gestão de recursos humanos: um estudo quantitativo das percepções dos gestores de RH sobre os trabalhadores jordanos e refugiados sírios em pequenas e médias empresas (PMEs) da Jordânia

Tese de doutoramento em Business AdministrationThe number of refugees has increased dramatically across the globe, but management research on this group of migrants is very scarce. This represents a unique opportunity for the field of HRM to contribute to a better understanding of refugees’ integration in the workplace and their particular challenges. The study takes the influx of Syrian refugees to Jordan as a challenge to HR managers and explores the HR practices implemented in Jordanian organizations that employ Syrian refugees. The main research objectives are twofold: i) to analyze four HR practices applied to Jordanian and Syrian workers, uncovering the main similarities/differences and ii) to explore the main challenges associated with each practice facing HRM and refugees. To accomplish the objectives of this research, the study surveyed HR managers of Small and Medium Enterprises (SMEs) in Jordan. Four major HR practices were studied: Recruitment and Selection (R&S), Organizational Integration and Socialization (OI&S), Training and Development (T&D), and Performance Appraisal System (PAS). The study followed a quantitative research approach with a descriptive research design. A non-probability sampling technique (convenience) was used to collect data. A self-administered questionnaire was distributed among HR managers or in charge of HRM in SMEs, and a total of 134 usable questionnaires were returned. The study made use of SPSS for descriptive and inferential statistics to analyze data and draw conclusions. Overall, the study findings suggest that respondents hold very positive perceptions of the current HR practices in their respective companies, which contradicts extant research in HRM conducted in Jordan. According to the surveyed HR managers, SMEs do not differentiate between Jordanian and Syrian employees. Nevertheless, some specific procedures and methodologies inherent to each HR practice are not applied (or applied to a lesser extent) to the later. Results also indicate that participants did not perceive the identified situations as challenges in R&S, OI&S, T&D and PAS. The implications of the study findings point out the importance of conducting more conceptual and empirical research to gain a better understanding about the HRM function in Jordan and provide country-specific recommendations to practitioners on how to tackle current and future challenges.O número de refugiados aumentou drasticamente em todo o mundo, mas as pesquisas de gestão sobre este grupo de migrantes são muito escassas. Isso representa uma oportunidade única para o campo da gestão de recursos humanos contribuir para uma melhor compreensão da integração dos refugiados no local de trabalho e seus desafios particulares. Considerando o afluxo massivo de refugiados sírios à Jordânia como um desafio para os gestores de RH, o estudo aborda as práticas de RH em organizações que empregam trabalhadores sírios. Os principais objetivos da pesquisa são: i) analisar quatro práticas de RH, no sentido de identificar as principais semelhanças/diferenças na sua aplicação a jordanos e refugiados sírios e ii) explorar os principais desafios associados a cada prática para a GRH e refugiados. Para tal, o estudo pesquisou gestores de RH de Pequenas e Médias Empresas (PMEs) na Jordânia. As quatro práticas de RH estudadas foram: Recrutamento e Seleção (R&S), Integração e Socialização Organizacional (I&SO), Formação e Desenvolvimento (F&D) e Sistema de Avaliação de Desempenho (SAD). O estudo seguiu uma abordagem de pesquisa quantitativa de natureza descritiva. Foi usada a técnica de amostragem por conveniência para a recolha de dados. Um questionário de auto-preenchimento foi distribuído entre gestores de RH ou responsáveis pela GRH nas PMEs, tendo-se recebido e um total de 134 respostas válidas. Recorreu-se ao SPSS para a análise estatística dos dados e tirar conclusões. Em termos globais, os resultados do estudo sugerem que os entrevistados têm percepções muito positivas das práticas de RH existentes nas respectivas empresas, o que contradiz a literatura no domínio da GRH realizada na Jordânia. De acordo com os gestores de RH alvo do estudo, as PMEs não diferenciam entre funcionários jordanos e sírios. No entanto, alguns procedimentos e metodologias específicas inerentes a cada prática de RH não se aplicam (ou aplicam-se em menor escala) aos sírios. Os resultados revelam ainda que os participantes não perceberam as situações apresentadas como desafios no âmbito das práticas estudadas. As implicações do estudo sublinham a importância de realizar investigação concetual e empírica mais aprofundada para obter uma melhor compreensão sobre a função de GRH na Jordânia e fornecer recomendações específicas dirigidas aos profissionais do país sobre como enfrentar os desafios atuais e futuros

Bare bones differential evolution

The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria

Adaptive Linkage Crossover

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual components, but in the interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generally chosen for problems. The strength of linkages between components of a chromosomal structure can be explicitly represented in a linkage matrix and used in the reproduction step to generate new individuals. For some problems, such a linkage matrix is known a priori from the nature of the problem. In other cases, the linkage matrix may be learned by successive minor adaptations during the execution of the evolutionary algorithm. This paper demonstrates the success of such an approach for several problems