MEASURING ECONOMIC GROWTH IN RURAL COMMUNITIES: THE SHIFT-SHARE APPROACH

Community/Rural/Urban Development,

Automated Privacy Protection for Mobile Device Users and Bystanders in Public Spaces

As smartphones have gained popularity over recent years, they have provided usersconvenient access to services and integrated sensors that were previously only available through larger, stationary computing devices. This trend of ubiquitous, mobile devices provides unparalleled convenience and productivity for users who wish to perform everyday actions such as taking photos, participating in social media, reading emails, or checking online banking transactions. However, the increasing use of mobile devices in public spaces by users has negative implications for their own privacy and, in some cases, that of bystanders around them. Specifically, digital photography trends in public have negative implications for bystanders who can be captured inadvertently in users’ photos. Those who are captured often have no knowledge of being photographed and have no control over how photos of them are distributed. To address this growing issue, a novel system is proposed for protecting the privacy of bystanders captured in public photos. A fully automated approach to accurately distinguish the intended subjects from strangers is explored. A feature-based classification scheme utilizing entire photos is presented. Additionally, the privacy-minded case of only utilizing local face images with no contextual information from the original image is explored with a convolutional neural network-based classifier. Three methods of face anonymization are implemented and compared: black boxing, Gaussian blurring, and pose-tolerant face swapping. To validate these methods, a comprehensive user survey is conducted to understand the difference in viability between them. Beyond photographing, the privacy of mobile device users can sometimes be impacted in public spaces, as visual eavesdropping or “shoulder surfing” attacks on device screens become feasible. Malicious individuals can easily glean personal data from smartphone and mobile device screens while they are accessed visually. In order to protect displayed user content, anovel, sensor-based visual eavesdropping detection scheme using integrated device cameras is proposed. In order to selectively obfuscate private content while an attacker is nearby, a dynamic scheme for detecting and hiding private content is also developed utilizing User-Interface-as-an-Image (UIaaI). A deep, convolutional object detection network is trained and utilized to identify sensitive content under this scheme. To allow users to customize the types ofcontent to hide, dynamic training sample generation is introduced to retrain the content detection network with very few original UI samples. Web applications are also considered with a Chrome browser extension which automates the detection and obfuscation of sensitive web page fields through HTML parsing and CSS injection

Exploring Photo Privacy Protection on Smartphones

The proliferation of modern smartphone camera use in the past decade has resulted in unprecedented numbers of personal photos being taken and stored on popular devices. However, it has also caused privacy concerns. These photos sometimes contain potentially harmful information if they were to be leaked such as the personally identifiable information found on ID cards or in legal documents. With current security measures on iOS and Android phones, it is possible for 3rd party apps downloaded from official app stores or other locations to access the photo libraries on these devices without user knowledge or consent. Additionally, the prevalence of smartphone cameras in public has reduced personal privacy, as strangers are commonly photographed without permission. To mitigate the privacy risk posed by apps and unwanted public photos, this research project explores 3 main topics: developing a two-step method including permission analysis and system call analysis to identify the possibility of 3rd party applications accessing sensitive photos without user knowledge, developing an automated classifier to identify and protect private photos in smartphone media storage, and creating an accurate computer vision model for identifying bystanders in photos, so that their faces might be later blurred or otherwise obfuscated to protect their privacy. The resulting data from the system call analysis will hopefully improve public awareness on the vulnerabilities created by downloading untrustworthy apps. The private photo classifier and bystander detection model are able to achieve acceptable accuracy on the test datasets and can be used in future works to implement working systems to protect individual privacy in the aforementioned threat cases

Scaling analysis applied to the NORVEX code development and thermal energy flight experiment

A scaling analysis is used to study the dominant flow processes that occur in molten phase change material (PCM) under 1 g and microgravity conditions. Results of the scaling analysis are applied to the development of the NORVEX (NASA Oak Ridge Void Experiment) computer program and the preparation of the Thermal Energy Storage (TES) flight experiment. The NORVEX computer program which is being developed to predict melting and freezing with void formation in a 1 g or microgravity environment of the PCM is described. NORVEX predictions are compared with the scaling and similarity results. The approach to be used to validate NORVEX with TES flight data is also discussed. Similarity and scaling show that the inertial terms must be included as part of the momentum equation in either the 1 g or microgravity environment (a creeping flow assumption is invalid). A 10(exp -4) environment was found to be a suitable microgravity environment for the proposed PCM

Evaluation and comparison of anti-cancer activity of dapagliflozin and canagliflozin in oral cancer cell line: an in vitro study

Background: Cancer is rapidly evolving life-threatening ailment in the mankind due to changes in daily food intake and lifestyle changes. Oral carcinoma is 6th major cause of cancer death in the world and it is third major reason of cancer mortality in India. Every cell in the human body requires glucose for its metabolic energy. Besides normal cell, cancer cells also require the glucose for its endurance and multiplication. SGLT2 inhibitors which are aimed at diabetes therapy exhibited anticancer properties also in colon and pancreatic cancer lines. Present study aim is to evaluate the anticancer activity of SGLT2 inhibitors against oral cancer cell by MTT Assay.Methods: To evaluate the anticancer activity of SGLT2 inhibitors MTT Cytotoxic assay is performed as per standard protocols. Cancer cells were plated in 24-well plates and incubated at 370C with 5% CO2 condition. After convergence, samples are added to the plates in various concentrations and allowed to incubate then they are detached from the plates and cleansed with the reagents. The wells are coated with the dye and incubated. Later samples are analysed in UV-spectrophotometer.Results: Cytotoxic assay showed decrease in cell viability with increasing dose of SGLT2 inhibitors. IC50 values were determined graphically. The IC50 value of dapagliflozin is 400µg/ml and canagliflozin is 250µg/ml respectively after 24 hours of Assessment.Conclusions: The results of the current study give us an evidence that SGLT2 inhibitors dapagliflozin and canagliflozin exhibits anticancer property in Oral Cancer cell line

Draft Genome Sequence of Kocuria sp. Strain UCD-OTCP (Phylum Actinobacteria).

Here, we present the draft genome of Kocuria sp. strain UCD-OTCP, a member of the phylum Actinobacteria, isolated from a restaurant chair cushion. The assembly contains 3,791,485 bp (G+C content of 73%) and is contained in 68 scaffolds