Progger: an efficient, tamper-evident kernel-space logger for cloud data provenance tracking

Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data. Logging from the kernel space empowers security analysts to collect provenance from the lowest possible atomic data actions, and enables several higher-level tools to be built for effective end-to-end tracking of data provenance. Within the last few years, there has been an increasing number of proposed kernel space provenance tools but they faced several critical data security and integrity problems. Some of these prior tools' limitations include (1) the inability to provide log tamper-evidence and prevention of fake/manual entries, (2) accurate and granular timestamp synchronisation across several machines, (3) log space requirements and growth, and (4) the efficient logging of root usage of the system. Progger has resolved all these critical issues, and as such, provides high assurance of data security and data activity audit. With this in mind, the paper will discuss these elements of high-assurance cloud data provenance, describe the design of Progger and its efficiency, and present compelling results which paves the way for Progger being a foundation tool used for data activity tracking across all cloud systems

The role of the preoptic area in response to cocaine

The preoptic area of the hypothalamus is ideally suited to modulate the behavioral and neural response to drugs of abuse such as cocaine. The preoptic area is broken up into two major subregions the medial preoptic area (mPOA) and the lateral preoptic area (LPO), both of which send dense projections to mesolimbic dopamine system. Specifically, both project to the ventral tegmental area (VTA), a brain region implicated in drug-associated reward. Previous work has demonstrated the mPOA is involved in the behavioral and neural response to cocaine in female rats, however the mechanism through which the mPOA modulates response to cocaine is unclear. Whether or not the mPOA also plays a role in response to cocaine in male rats is still not clear. Furthermore the role of the adjacent LPO in response to cocaine is unexplored, despite its anatomical relationship to the VTA and involvement in intracranial self-stimulation. Here I demonstrate that estradiol acts in the mPOA of female rats to modulate response to cocaine. Specifically, microinjections of estradiol directly into the mPOA one day prior to cocaine administration increase cocaine-induced dopamine levels in the nucleus accumbens. The mPOA is also involved in the behavioral regulation of response to cocaine in male rats, as mPOA lesions enhanced cocaine-induced locomotion and reward. Finally, activation of the LPO, with pharmacology or chemogenetics, potentiates reinstatement of cocaine seeking, an animal model of drug relapse. Together these results demonstrate that the preoptic area as a whole is involved in the regulation of the neural and behavioral response to cocaine and shed light on underlying regulatory mechanisms.Psycholog

Mapping Soil Organic Carbon (SOC) in a Semi-Arid Mountainous Watershed Using Variables From Hyperspectral, Lidar and Traditional Datasets

Quantifying soil organic carbon (SOC) in complex terrain is challenging due to its high spatial variability. Generally, limited discrete observations of SOC data are used to develop spatially distributed maps of SOC by developing quantitative relationships between SOC and available spatially distributed variables. In many ecosystems, remotely sensed information on aboveground vegetation can be used to predict belowground carbon stocks. In this research, we developed maps of SOC across a semi-arid watershed based on discrete field observations and modeling using a suite of variables inclusive of hyperspectral and lidar datasets; these observations provide insights into the controls on soil carbon in this environment. The Reynolds Creek Experimental Watershed (RCEW), in SW Idaho, has a strong elevation gradient that controls precipitation and vegetation. Soil samples were collected to 30 cm depth using a nested sampling approach, across the watershed (samples, 279 data points, in 28 plots, discretized with depth, total n=1344) and analyzed for SOC content. Point SOC data was combined with a suite of predictor variables from traditional, lidar and hyperspectral datasets to calibrate Random Forest and Stepwise Multiple Linear Regression models that predict SOC distribution across RCEW. In this study, SOC generally increased along the precipitation-elevation gradient corresponding with an increase in the diversity and abundance of vegetation. We found that variable soil bulk densities and areas of high rock content strongly influenced mass/unit area SOC values. Interestingly, rock content was also negatively correlated with percent SOC. Local variability of SOC in this study was high with the variability at the plot scale about 1/3 of that observed at the watershed scale. Our research suggests that vegetation indices calculated from spectral data are the best predictors of SOC storage in this system. Roughly 60% of the variance in SOC data is explained using Normalized Difference Vegetation Index while two hyperspectral vegetation indices, Modified Red Edge Simple Ratio and Modified Red Edge Normalized Difference Vegetation Index explain over 70%. The addition of Lidar variables modestly improved SOC prediction, explaining 75% of variability in SOC

Fourth Down Decision Making: Challenging the Conservative Nature of NFL Coaches

This thesis analyzes the hypothesis that coaches in the National Football League are often too conservative in their decision making on fourth downs. I used R Studio and NFL play-by-play data to simulate actual football plays and drives according to different fourth down strategies. By measuring expected points per drive over thousands of simulated drives, we are able to evaluate the effectiveness of different fourth down strategies. This research points to a number of conclusions regarding the nature of NFL coaches on fourth downs as well as the complexity of modeling and simulating decision making in a complex sport such as professional football. While we are able to demonstrate areas where a more aggressive fourth down strategy could be utilized to a team’s advantage, this research demonstrates that fourth down decision is not a simple binary choice and that making this critical decision must be taken in context. In other words, further research should be done that takes into account additional variables and their impact on a team’s decision to “go for it” or not on fourth down

Assessment of Hand Gestures Using Wearable Sensors and Fuzzy Logic

Hand dexterity and motor control are critical in our everyday lives because a significant portion of the daily motions we perform are with our hands and require some degree of repetition and skill. Therefore, development of technologies for hand and extremity rehabilitation is a significant area of research that will directly help patients recovering from hand debilities sustained from causes ranging from stroke and Parkinson’s disease to trauma and common injuries. Cyclic activity recognition and assessment is appropriate for hand and extremity rehabilitation because a majority of our essential motions are cyclic in their nature. For a patient on the road to regaining functional independence with daily skills, the improvement in cyclic motions constitutes an important and quantifiable rehabilitation goal. However, challenges exist with hand rehabilitation sensor technologies preventing acquisition of long-term, continuous, accurate and actionable motion data. These challenges include complicated and uncomfortable system assemblies, and a lack of integration with consumer electronics for easy readout. In our research, we have developed a glove based system where the inertial measurement unit (IMU) sensors are used synergistically with the flexible sensors to minimize the number of IMU sensors. The classification capability of our system is improved by utilizing a fuzzy logic data analysis algorithm. We tested a total of 25 different subjects using a glove-based apparatus to gather data on two-dimensional motions with one accelerometer and three-dimensional motions with one accelerometer and two flexible sensors. Our research provides an approach that has the potential to utilize both activity recognition and activity assessment using simple sensor systems to help patients recover and improve their overall quality of life

The WISEWOMAN Program: Reflection and Forecast

The WISEWOMAN program targets low-income under- and uninsured women aged 40–64 years for screening and interventions aimed at reducing the risk of heart disease, stroke, and other chronic diseases. The program enters its third phase on June 30, 2008. Design issues and results from Phase I and Phase II have been published in a series of papers. We summarize remaining challenges, which were identified through systematic research and evaluation. Phase III will address these challenges through a number of new initiatives such as allowing interventions of different intensities, taking advantage of resources for promoting community health, and providing evidence-based interventions through the program's Center of Excellence. Finally, we provide a framework and vision so that organizational, community, and other partners can make the case for the importance of the program to their communities and for what is needed to make it work

Optimizing Process-Based Models to Predict Current and Future Soil Organic Carbon Stocks at High-Resolution

From hillslope to small catchment scales (\u3c 50 km2), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m2) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics

Play the Game: Evaluation of Psychological Capital, Emotional Intelligence, and Virtual Team Performance among Online Gaming Teams

Virtual teams are becoming ubiquitous with the contemporary work environment. Shutdowns due to the COVID-19 pandemic necessitated many to reconceive the workplace whereby people accomplished work no longer located in immediate proximity, leading many teams to engage online in virtual spaces. Even in a post-lockdown world, virtual teams have remained widely used within the workplace. Online gaming has exploded in popularity, it allows people to interact with others from across the world. Numerous parallels exist between online gaming teams and problem-solving teams often used in the contemporary workplace: 1) pursuing a shared goal; 2) identifying member roles; and 3) collaborating together to accomplish a task. The purpose of our research is to examine the role that psychological capital and emotional intelligence play in team success within an online gaming team challenge. Participants will compete in an online video game event, where their goal, as a 6-player team, is to solve a set of challenges within 24 hours. The researchers will conduct a content analysis of participant recordings from the event. Findings and implications will be discussed. Understanding member interaction among online gaming teams has potential implications for how workplace teams can interact more effectively together