Social Media, Privacy, and the Academic Classroom

We live in a world full of social media and portable technology that allows for the effortless access to, and sharing of, information. While this constant connection can be viewed as a benefit by some, there have been recent, sometimes embarrassing, instances throughout the world that show just how quickly any expectation of privacy can be destroyed. From pictures of poorly dressed shoppers at a grocery store to customers recording interactions with their servers at restaurants, the internet is full of media (all with the potential to go viral) created and posted without consent of all parties captured. This risk to privacy is not just limited to retail and restaurants, as being in any situation amongst people puts you at risk, including being in an academic classroom. Anyone providing in-class instruction, be they professor or librarian, can be at risk for this type of violation of privacy. In addition, the students in the class are also at risk for being unwittingly captured by their classmates. To combat this, colleges and universities are providing recommendations to faculty regarding this issue, such as including suggested syllabus statements about classroom recording by students. In some instances, colleges and universities have instituted formal policies with strict penalties for violators. An overview of current privacy law as it relates to an academic setting is discussed as well as recent, newsworthy instances of student recording in the classroom and the resulting controversies. Additionally, there is a discussion highlighting various recommendations and formal policies that have been issued and adopted by colleges and universities around the country. Finally, advice is offered about what librarians can do to educate students, faculty, and staff about the privacy rights of others and the potential harm that could come from posting to social media and the open web images and video of others without their consent.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Newly Automated System For Integrated Assessment Of The Conditions Of Underwater Gas Pipelines

An underwater gas pipeline is the portion of the pipeline that crosses a river beneath its bottom. Underwater gas pipelines are subject to increasing dangers as time goes by. An accident at an underwater gas pipeline can lead to technological and environmental disaster on the scale of an entire region. Therefore, timely troubleshooting of all underwater gas pipelines in order to prevent any potential accidents will remain a pressing task for the industry. The most important aspect of resolving this challenge is the quality of the automated system in question. Now the industry doesn't have any automated system that fully meets the needs of the experts working in the field maintaining underwater gas pipelines. Principle Aim of this Research: This work aims to develop a new system of automated monitoring which would simplify the process of evaluating the technical condition and decision making on planning and preventive maintenance and repair work on the underwater gas pipeline. Objectives: Creation a shared model for a new, automated system via IDEF3; Development of a new database system which would store all information about underwater gas pipelines; Development a new application that works with database servers, and provides an explanation of the results obtained from the server; Calculation of the values MTBF for specified pipelines based on quantitative data obtained from tests of this system. Conclusion: The new, automated system PodvodGazExpert has been developed for timely and qualitative determination of the physical conditions of underwater gas pipeline; The basis of the mathematical analysis of this new, automated system uses principal component analysis method; The process of determining the physical condition of an underwater gas pipeline with this new, automated system increases the MTBF by a factor of 8.18 above the existing system used today in the industry.