Sub-Microarcsecond Astrometry with SIM-Lite: A Testbed-based Performance Assessment

SIM-Lite is an astrometric interferometer being designed for sub-microarcsecond astrometry, with a wide range of applications from searches for Earth-analogs to determining the distribution of dark matter. SIM-Lite measurements can be limited by random and systematic errors, as well as astrophysical noise. In this paper we focus on instrument systematic errors and report results from SIM-Lite's interferometer testbed. We find that, for narrow-angle astrometry such as used for planet finding, the end-of-mission noise floor for SIM-Lite is below 0.035 uas.Comment: 5 pages, 5 figure

A Human Centered Framework for Information Security Management: A Healthcare Perspective

Research on the human element of information security is fragmented at best. This paper presents a management framework for organizations in the health care industry who wish to improve their information security procedures in an effort to comply with HIPAA and other regulations. The emphasis is on securing an organization from internal threats by adequately educating employees and building an organizational culture where security initiatives are valued and respected. The premise of the paper is that a cultural approach is the only way to gain the versatile security environment needed to comply with regulations as vast and complex as HIPAA. We argue that this framework demands that empirical data be collected through careful industry research with health care providers so as to prove the real world value of its application

From Hashtags to Movements: A Framing Perspective of The Role of Social Media in the Emergence and Development of Impactful Social Movements

Social media plays a critical role in social movement activities. This study takes a framing perspective to investigate how social media affordances support the process of creation, communication, and negotiation of frames and assess the impact of the framing process on social movement outcomes

virtFlow: guest independent execution flow analysis across virtualized environments

An agent-less technique to understand virtual machines (VMs) behavior and their changes during the VM life-cycle is essential for many performance analysis and debugging tasks in the cloud environment. Because of privacy and security issues, ease of deployment and execution overhead, the method preferably limits its data collection to the physical host level, without internal access to the VMs. We propose a host-based, precise method to recover execution flow of virtualized environments, regardless of the level of virtualization. Given a VM, the Any-Level VM Detection Algorithm (ADA) and Nested VM State Detection (NSD) Algorithm compute its execution path along with the state of virtual CPUs (vCPUs) from the host kernel trace. The state of vCPUs is displayed in an interactive trace viewer (TraceCompass) for further inspection. Then, a new approach for profiling threads and processes inside the VMs is proposed. Our proposed VM trace analysis algorithms have been open-sourced for further enhancements and to the benefit of other developers. Our new techniques are being evaluated with workloads generated by different benchmarking tools. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1%) as compared to other approaches

Effect of Explainable Artificial Intelligence and Decision Task Complexity on Human-Machine Symbiosis

Artificial Intelligence (AI) is a tool that augments various facets of decision making. This disruptive technology is helping humans perform better and faster with accuracy (Grigsby 2018). There are tasks where AI decides in real-time without human intervention. For example, AI can approve or decline a credit card application without any human intervention. On the other hand, there are tasks where both AI and human reasoning is required to make the decision. For instance, automated employee selection decision requires a higher level of human involvement. Interaction between humans and machines is required in such decisions. Grigsby (2018) posits that the interaction becomes effective when the machine understands human and human understands machine. This interplay is called human-machine symbiosis that merges the best of the human with the best of the machine. The human decision-makers need to understand how the machine is reaching to a specific prediction. One tool that facilitates this understanding by increasing the interpretability of the algorithm is Explainable AI (XAI). XAI is a tool that explains the results to the decision-maker in a human-understandable manner (Rai 2020). As a result, the decision is more transparent and fairer. Other than the benefits of transparency and fairness, there is an emerging regulatory requirement for explaining machine-driven decisions. The General Data Protection Regulation addresses the right to explanation by enabling the individuals to ask for an explanation for algorithm’s output (Selbst and Powles 2017). That is why the decision-makers need to convert their decision-making tool from a black box to a glass box. To enhance the explainability and interpretability, two broad categories of XAI techniques are model-specific XAI and model-agnostic XAI (Rai 2020). The model-specific techniques incorporate interpretability in the inherent structure of the learning model whereas the model-agnostic techniques use the learning model as an input to generate explanation. These models ensure transparency and fairness in human-machine decision making. Another important factor for effective human-machine symbiosis is decision task complexity (Grigsby 2018). Task complexity in decision making can be characterized by the number of desired outcomes, conflicting interdependencies among outcomes, path multiplicity, and uncertainty (Campbell 1988). When the decision-making task is unstructured and complicated, then the decision-maker’s need for understanding the algorithmic process increases. Moreover, decision task complexity is a factor of trust in the autonomous system, and trust is a factor of human-machine symbiosis (Grigsby 2018). Furthermore, decision task complexity is related to the mental workload and cognitive ability of the decision-makers (Grigsby 2018; Speier and Morris 2003). In the extant literature, there is a gap in explaining how the interplay between XAI techniques and decision task complexity impacts the decision makers perception about the human-machine symbiosis. Therefore, the objective of this research is to investigate the effect of XAI and decision task complexity on perceived human-machine symbiosis. Using the theories of information overload and algorithmic transparency, we develop a causal model to explain the relationship. We will run a randomized 2×2 factorial experiment to test the model. The paper will have theoretical and practical implications

Effects of Mentha pulegium water extract dipping on quality and shelf life of silver carp (Hypophthalmichthys molitrix) during superchilled storage

The effects of Mentha pulegium water extract dipping on quality and shelf life of silver carp during superchilled storage were investigated. Fish samples were treated with water extract of 1 and 3% M. pulegium, and then stored at -3 οC for 30 days. The control and the treated fish samples were analyzed periodically for chemical (pH, PV, TBA, TVB-N), and sensory characteristics. The results indicated that the effect of M. pulegium extract dipping on fish samples was to retain their good quality characteristics and extend the shelf life during superchilled storage, which was supported by the results of chemical and sensory evaluation analyses. In this respect, the sample supplemented with 3% water extract was more potent compared with the 1% one in extending the shelf life of fish fillets

VM processes state detection by hypervisor tracing

The diagnosis of performance issues in cloud environments is a challenging problem, due to the different levels of virtualization, the diversity of applications and their interactions on the same physical host. Moreover, because of privacy, security, ease of deployment and execution overhead, an agent-less method, which limits its data collection to the physical host level, is often the only acceptable solution. In this paper, a precise host-based method, to recover wait state for the processes inside a given Virtual Machine (VM), is proposed. The virtual Process State Detection (vPSD) algorithm computes the state of processes through host kernel tracing. The state of a virtual Process (vProcess) is displayed in an interactive trace viewer (Trace Compass) for further inspection. Our proposed VM trace analysis algorithm has been open-sourced for further enhancements and for the benefit of other developers. Experimental evaluations were conducted using a mix of workload types (CPU, Disk, and Network), with different applications like Hadoop, MySQL, and Apache. vPSD, being based on host hypervisor tracing, brings a lower overhead (around 0.03%) as compared to other approaches

Virtual CPU state detection and execution flow analysis by host tracing

Cloud computing offers to the end user the ability of accessing a pool of resources with the Pay as Use (PaU) model. By leveraging this technology, users can benefit from hardware virtualization for on-demand resource acquisition and rapid elasticity. However, there is no effective tool to analyze virtual hardware performance, especially when isolation between these virtual resources is not adequate. The existing tools need to access and trace the whole activity of the VM and host. However, in most cases, tracing the virtual machine (VM) is not possible because of security issues and the added overhead. Therefore, there is a need for a tool to troubleshoot unexpected behavior of VMs without internal access for tracing or debugging. In this paper, we propose a new method to study the state of CPUs inside VMs without internal access. Our tool can detect unexpected delays and their root causes. We developed a virtual CPU (vCPU) state analyser to detect the state of vCPUs along with the reason for being in that state. This approach relies on host tracing, thus adding less overhead to VMs as compared to existing approaches. Then we propose a new approach for profiling threads inside the VMs by host tracing. We implemented different views for the TraceCompass trace viewer to let the administrator visually track different threads and their states inside the VMs. Our tool can detect different problems such as overcommitment of resources

Impact of Culture on Knowledge Management: A Meta-Analysis and Framework

Culture, both national and organizational, can have profound impacts on knowledge management. Yet the literature on exactly how culture impacts knowledge management is complex with no clear generalizable results. A meta-analysis was conducted on 52 articles from ten IS journals for the years 2000–2010 combining both quantitative and qualitative studies in a unique methodological approach. Key findings include a marked shift away from normative language towards more interpretive and critical discourse emphasizing the power issues inherent in the cultural context of knowledge management. Trust and openness are key organizational cultural dimensions that impact knowledge management processes, but these traits are achieved through effective business leadership, rather than a particular technological artifact. The most striking generalizable finding from the cross-case analysis is that organizational culture can overcome or mitigate differences in national culture. An overall framework is provided to illustrate the findings and to serve as an important guidepost for future research