People with severe motor disabilities are not able to move their limbs voluntarily and speech overtly, though the cognitive parts of their brain are intact. Human computer interfaces, as an assistive technology, provide a new channel of communication to help these people. In this study, a novel wearable miniaturized human computer interface system was designed and tested allowing these people to state their intentions and feelings just by using their eyes. The system that can be installed on glasses, records the electrooculogram signal and transfers the digitized data wirelessly to a laptop. By analyzing the signals, eight directions of eye movements consist of up, down, right, left and four diagonal directions, as well as the voluntary blinking were recognized and used in a high performance graphical user interface to type alphabetical letters and numbers, just by two moves and two selections. Two experiments were conducted to evaluate the performance of the developed system. Precision, sensitivity and accuracy of recognizing the user intention were obtained 95%, 98% and 93% respectively and the average rate of communication was 5.88 character per minute. This low-cost wearable device is light-weight with small size which assures high level of mobility and comfort. The users could learn to type with the system in a short time, and easily work with it without fatigue.
Cloud computing provides on-demand resources and removes the boundaries of resources' physical locations. By providing virtualized computing resources in an elastic manner over the internet, IaaS providers allow organizations to save upfront infrastructure costs and focus on features that discriminate their businesses. The growing number of providers makes manual selection of the most suitable configuration of IaaS resources, or IaaS services, difficult and time consuming while requiring a high level of expertise. In our previous paper we proposed QuARAM recommender, a general platform for automatic IaaS service selection. In this paper, we present in detail the hybrid approach to automatic service selection used in our platform. The selection process begins with automatic extraction of an application's features, requirements and preferences, which are then used to produce a list of potential services for the application's deployment. We use case-based reasoning and MCDM (Multi-criteria Decision Making) to provide a recommendation of suitable services for application deployment, clustering to handle the problem of a large search space and a service consolidation method to improve the resource utilization and decrease the total service price. We carry out a case study with a prototype implementation of our platform to demonstrate that automatic IaaS service selection using a combination of all the proposed approaches is both practical and achievable.
Information systems today have become incredibly complex and span multiple organizational networks, database and applications servers and on to the external Internet cloud resources. Consequently strategic approaches are needed to troubleshoot system failures by first identifying the component causing the failure, and thereby, further investigating the cause of the failure to solve the problem. Information regarding past troubleshooting strategies can be used to provide guidance for solving similar problems. We present a framework, DSDAware (Decision Support for Database Administrators using Warehouse-as-a-service) for developing a Decision Guidance and Support System (DGSS). The framework dynamically extracts knowledge from various correlated data sources containing systems related data and from the problem solving procedures of the human experts. The knowledge is used in a strategic problem solving approach to train new administrators by guiding them through the troubleshooting process using an interactive interface, and to offer a decision support service to the Web community. Our work specifically focuses on z/OS Mainframe DB2 database (DB) problems where the inherent complexity of the system makes troubleshooting a challenging task. The diminishing population of mainframe DB administrators (DBA) asserts the need for a DGSS for the new DBAs. The research applies text and data mining techniques for knowledge extraction, a rule-based system for knowledge representation and problem categorization, and a case-based system for providing decision support.
Introduction: Violence against women is a global public health problem. Although there has been much research done on violence against women there are few studies to provide of the current scientific production.Method: In this study, bibliometric analysis has been used to evaluate the 1,984 documents from 1986 to 2020 based on SCOPUS databases. These documents were analyzed quantitatively by Bibliometric R Package and VOSviewer software. In addition, the 20 top-cited papers were analyzed qualitatively.Results: The research findings show that the United States is a leader in this field with the most highly cited articles and also the most number of publications followed by the UK, Canada, Australia, and South Africa. A total of 1,984 documents were collected from the Scopus database and were analyzed in bibliometric Research Package and VOSviewer. Results showed that the average citations per year for each document were 23.39% and the annual scientific production growth rate was 16.86%. Keywords analysis indicates that most articles focus on sexual violence”, "sexual assault”, intimate partner violence”, “violence against women”, “sexual abuse”, “domestic violence”, “child sexual abuse”, “prevention” and “rape”. Sources such as the “Journal of Interpersonal Violence”, “Journal of Violence Against Woman”, “Journal of Violence and Victims”, “Psychology of Women Quarterly”, “Journal of Adolescent Health”, “Journal of Consulting and Clinical Psychology”, and “American Journal of Public Health”, “Journal of Consulting and Clinical Psychology” and “American journal of Public Health and Lancet” are the top most productive in this field. Conclusion: Examining the articles showed that the vast majority of women have experienced verbal, sexual, intimate partner violence, cyber harassment, etc.
Cloud computing enables elastic resource provisioning on demand and removes the boundaries of resources' physical locations. The number of cloud-based services is on the rise due to the growing interest from both providers and consumers. These services are characterized by a large number of features or properties, which makes the automatic service selection and deployment challenging. This paper proposes QuRAM Recommender, a cloud infrastructure service recommender framework based on case-based reasoning (CBR) that supports effective service selection. QuRAM Recommender supports decision making that accommodates the customer's preferences and feedback. We show the feasibility of our approach through a prototype implementation that elaborates on the main features of our system. The experimental results suggest that case-based reasoning is a viable option for recommending cloud services that best fit the customer's requirements.
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