Rheumatic Arthritis (RA) is the most common disease found in the majority

Personal video conferencing has become the new norm after COVID-19 caused a seismic shift from in-person meetings and phone calls to video conferencing for daily communications and sensitive business. Video leaks participants' onscreen information because eyeglasses and other reflective objects unwittingly expose partial screen contents. Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual information gleamed from eyeglass reflections captured by webcams. The primary goal of our work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves in the future. Our work explores and characterizes the viable threat models based on optical attacks using multi-frame super resolution techniques on sequences of video frames. Our experimental results and models show it is possible to reconstruct and recognize on-screen text with a height as small as 10 mm with a 720p webcam. We further apply this threat model to web textual content with varying attacker capabilities to find thresholds at which text becomes recognizable. Our user study with 20 participants suggests present-day 720p webcams are sufficient for adversaries to reconstruct textual content on big-font websites. Our models further show that the evolution towards 4K cameras will tip the threshold of text leakage to reconstruction of most header texts on popular websites. Our research proposes near-term mitigations, and justifies the importance of following the principle of least privilege for long-term defense against this attack. For privacy-sensitive scenarios, it's further recommended to develop technologies that blur all objects by default, then only unblur what is absolutely necessary to facilitate natural looking conversations.

Forecasting economic strength from several economic indicators is the primary concern of the area of econometrics. Gross domestic product measures the current value of all goods within a country and is one of the most prominent economic parameters used to evaluate economic development. With the understanding of how economic indicators affect the economy, planners can choose to allocate resources in certain industries to boost economic growth. This study builds an econometric model of US GDP with back-propagation artificial neural network architecture. Taking data from more than the past 70 years, this model will use machine learning to understand the behavior of three economic sectors of the United States. By analyzing the interconnecting behavior of the three sectors, it can form a mathematical relationship between them and produce an output of US GDP. This model accounts for nonlinearity and can be easily adapted to include more economic indicators than just the three sectors selected, sharpening the results and allowing the study of new relationships. After the machine learning process is complete, economists can adjust the input values to examine its effect on the resulting GDP from the derived mathematical relationship. The model's current implementation is found to be very satisfactory and can be useful for the future planning of economic activity.

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