Software Obfuscation with Symmetric Cryptography

Software protection is of great interest to commercial industry. Millions of dollars and years of research are invested in the development of proprietary algorithms used in software programs. A reverse engineer that successfully reverses another company‘s proprietary algorithms can develop a competing product to market in less time and with less money. The threat is even greater in military applications where adversarial reversers can use reverse engineering on unprotected military software to compromise capabilities on the field or develop their own capabilities with significantly less resources. Thus, it is vital to protect software, especially the software’s sensitive internal algorithms, from adversarial analysis. Software protection through obfuscation is a relatively new research initiative. The mathematical and security community have yet to agree upon a model to describe the problem let alone the metrics used to evaluate the practical solutions proposed by computer scientists. We propose evaluating solutions to obfuscation under the intent protection model, a combination of white-box and black-box protection to reflect how reverse engineers analyze programs using a combination white-box and black-box attacks. In addition, we explore use of experimental methods and metrics in analogous and more mature fields of study such as hardware circuits and cryptography. Finally, we implement a solution under the intent protection model that demonstrates application of the methods and evaluation using the metrics adapted from the aforementioned fields of study to reflect the unique challenges in a software-only software protection technique

Effectiveness of Global Postural Re-Education in Chronic Non-Specific Low Back Pain: Systematic Review and Meta-Analysis

Background: The aim of this systematic review and meta-analysis was to evaluate the global postural re-education (GPR) program's effectiveness compared to other exercise programs in subjects with persistent chronic low back pain. Methods: A systematic review and meta-analysis were carried out using PRISMA2020. An electronic search of scientific databases was performed from their inception to January 2021. Randomized controlled trials that analyzed pain and patient-reported outcomes were included in this review. Four meta-analyses were performed. The outcomes analyzed were disability due to back pain and pain. The risk of bias and quality of evidence were evaluated. The final search was conducted in March. Results: Seven trials were included, totaling 334 patients. The results showed improvement in pain measured by Visual Analogue Scale (VAS) (Standardised Mean Difference (SMD) = -0.69; 95% Confidence Interval (CI), -1.01 to -0.37; p < 0.0001), Numerical Pain Scale (NRS) (SMD = -0.40; 95% CI, -0.87 to 0.06); p = 0.022), VAS + NRS (SMD = -1.32; 95% CI, -1.87 to -0.77; p < 0.0001) and function (Roland Morris Disability Questionnaire (RMDQ)) (SMD = -0.55; 95% CI, -0.83 to -0.27; p < 0.0001) after GPR treatment. Conclusion: This meta-analysis provides reliable evidence that GPR may be an effective method for treating LBP by decreasing pain and improving function, with strong evidence.This research was partially financially supported by the Erasmus+ Strategic Partnership for Higher Education Programme (Key Action 203) [Grant number: 2018-1-PL01-KA203-051055]

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

The role of product portfolio management in market expansion:a case of the mobile gaming industry

Abstract. The rapid growth of mobile game consumer spending has led to Free-to-Play mobile game developers’ constant competition for players by offering new games. The product portfolio management (PPM) approach helps tackle questions about the market, product and technologies based on a company’s strategic targets. However, to discover game genre diversity by aligning product portfolio with business strategy and existing capabilities in new product development process is challenging. A single-case study was conducted to examine the important connection between PPM and business strategy as well as existing capabilities to propose a practical approach for seeking game genre portfolio expansion opportunities. The main results include proposing an analysis framework using PPM and mobile app intelligence software to identify game genres in market expansion that are strategic fit, bring the best economic value and are resonated with company’s existing capabilities and competence. PPM focused areas and key performance indicators are proposed. This study is the first attempt to apply PPM approach with targets and KPIs in mobile game development. It contributes to the previous studies by extending the application of PPM approach in the initial stage of product development process in discoveries and innovation stage. Also, the results can be applied to other mobile game companies with similar new product development process

The effectiveness of physical rehabilitation in the enhancement of proprioceptive and cognitive aspects on Alzheimer disease patients.

Based on the Alzheimer Disease (AD) prevention and slowing down, this study has shown interest in evaluating the effects of Global Postural Re-education (GPR) on the cognitiveness of individuals with AD. OBJECTIVE: It is important to verify that by modifying and improving postural attitudes, a better concentration of cognitions in older people is achieved, increases self-awareness and proprioception. MATERIALS AND METHODS: This research study is based on an experimental design where participated 135 subjects with AD. It lasted 6 months, with pre-post tests executed before and after the period of treatment. RESULTS: The therapy had a significant effect on the Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Quality of life in AD (QoL-AD), Barthel Index (BI), Neuropsychiatric Inventory (NPI) and Tinetti Scale scores (TS) compared to the ones of group factor. In the findings of post-hoc analysis it was observed: the improvement of treatment variables, MMSE scores, GDS, QoL -AD and BI (p corrected by Bonferroni <0.005 in all cases). Nonetheless, the improvement was also observed from the first month of therapy in TS scores and NPI (p corrected by Bonferroni <0.005 in all cases). CONCLUSIONS: This study confirms the validity of the GPR proposal on the cognitiveness of individuals with AD.Actividad Física y DeporteMedicinaTerapia y Rehabilitació

Long-Term Simultaneous Localization and Mapping in Dynamic Environments.

One of the core competencies required for autonomous mobile robotics is the ability to use sensors to perceive the environment. From this noisy sensor data, the robot must build a representation of the environment and localize itself within this representation. This process, known as simultaneous localization and mapping (SLAM), is a prerequisite for almost all higher-level autonomous behavior in mobile robotics. By associating the robot's sensory observations as it moves through the environment, and by observing the robot's ego-motion through proprioceptive sensors, constraints are placed on the trajectory of the robot and the configuration of the environment. This results in a probabilistic optimization problem to find the most likely robot trajectory and environment configuration given all of the robot's previous sensory experience. SLAM has been well studied under the assumptions that the robot operates for a relatively short time period and that the environment is essentially static during operation. However, performing SLAM over long time periods while modeling the dynamic changes in the environment remains a challenge. The goal of this thesis is to extend the capabilities of SLAM to enable long-term autonomous operation in dynamic environments. The contribution of this thesis has three main components: First, we propose a framework for controlling the computational complexity of the SLAM optimization problem so that it does not grow unbounded with exploration time. Second, we present a method to learn visual feature descriptors that are more robust to changes in lighting, allowing for improved data association in dynamic environments. Finally, we use the proposed tools in SLAM systems that explicitly models the dynamics of the environment in the map by representing each location as a set of example views that capture how the location changes with time. We experimentally demonstrate that the proposed methods enable long-term SLAM in dynamic environments using a large, real-world vision and LIDAR dataset collected over the course of more than a year. This dataset captures a wide variety of dynamics: from short-term scene changes including moving people, cars, changing lighting, and weather conditions; to long-term dynamics including seasonal conditions and structural changes caused by construction.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111538/1/carlevar_1.pd