Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Effect of Self-Healing Concrete on Building Durability in the UK Construction Market

This study explores the effect of self-healing concrete in existing buildings and its crucial impact in terms of sustainability, permanence, and access to the internal strength of concrete in order to contribute to the process of protecting the internal parts of the building from the reinforcing steel structure as well as prevent the internal corrosion of iron reinforcement. This study also highlights the market growth side of the cement sector for self-healing products and access to growth in markets from (24 to 26%). Self-healing concrete technology is an advanced technology with self-healing compound. It allows for the repair of small cracks that affect the structure, which contributes to the support of the building to repair and rehabilitate the building. It also helps in repairing internal cracks without any financial costs. Compound prepares to spread when cracks occur and when reaching an appropriate environment in order to work to rehabilitate cracks. In the coming years, as growth thrives in order to reach the purchasing power in a prosperity in the cement sector, which improves internal and external investment and increases in attracting investors in the cement sector. The study aims to reach scientific research of high quality and it specializes in studying the United Kingdom market and the prosperity of self-healing concrete in the cement sector. Moreover, it contributes to the prosperity of self-healing products because of their contribution in reducing the financial costs that are under the building maintenance and rehabilitation department. This study also contributes to studies in the United Kingdom, which resulted from the impact of the cement market that will probably flourish and grow in the coming years. The researcher suggests that the development of the self-healing concrete will be reflected in the cement sector, which will be a reason for improving and developing self-healing concrete that will spread in sectors other than the cement industry

Case Study: Securing MMU-less Linux Using CHERI

MMU-less Linux variant lacks security because it does not have protection or isolation mechanisms. It also does not use MPUs as they do not fit with its software model because of the design drawbacks of MPUs (\ie coarse-grained protection with fixed number of protected regions). We secure the existing MMU-less Linux version of the RISC-V port using CHERI. CHERI is a hardware-software capability-based system that extends the ISA, toolchain, programming languages, operating systems, and applications in order to provide complete pointer and memory safety. We believe that CHERI could provide significant security guarantees for high-end dynamic MMU-less embedded systems at lower costs, compared to MMUs and MPUs, by: 1) building the entire software stack in pure-capability CHERI C mode which provides complete spatial memory safety at the kernel and user-level, 2) isolating user programs as separate ELFs, each with its own CHERI-based capability table; this provides spatial memory safety similar to what the MMU offers (\ie user programs cannot access each other's memory), 3) isolating user programs from the kernel as the kernel has its own capability table from the users and vice versa, and 4) compartmentalising kernel modules using CompartOS' linkage-based compartmentalisation. This offers a new security front that is not possible using the current MMU-based Linux, where vulnerable/malicious kernel modules (\eg device drivers) executing in the kernel space would not compromise or take down the entire system. These are the four main contributions of this paper, presenting novel CHERI-based mechanisms to secure MMU-less embedded Linux

Dynamic Conditional Imitation Learning for Autonomous Driving

Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at https://heshameraqi.github.io/dynamic_cil_autonomous_driving.Comment: 14 pages, 11 figures, 7 table

Peer learning on a shoe string: success of a distributive model for peer learning in a community radiology practice.

PURPOSE:To describe our 3-year experience operating a peer learning program with minimal resources, calculate the cost of the program, and compare participants' attitudes toward peer learning to the historical peer review system. MATERIALS AND METHODS:The peer learning conference is held monthly for 1 h via a web-based video conferencing platform. Case identification, curation, and conference presentation are performed by individual radiologists. Using national estimates for unit costs of radiologist time and other inputs, we calculated the marginal cost of the peer learning program to the medical group. After 21 months of holding the conference, we conducted an anonymous survey to assess the impact of the conference and how it may be improved. RESULTS:A peer learning conference was established for a 24- person community-based practice, which is part of a large multi-disciplinary medical group. Cases discussed included diagnostic errors, technologist errors, good calls, and challenging cases. Total annual cost of the program is $3288. Survey respondents had overall positive views of the conference and strongly preferred peer learning to the existing peer review system in place, with 80% of respondents rating peer learning more favorably than peer review on education value, supportive environment, and punitive process, and 70% more positively on culture of blaming. CONCLUSION:We established a peer learning program with minimal resources. Radiologists in the group strongly prefer the peer learning conference over the existing peer review program in place. A peer learning program can be successfully started and sustained with limited resources

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

Background: Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. Methods: The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. Results: The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. Conclusion: ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

Diffusion Tensor Imaging in a Large Longitudinal Series of Patients With Cervical Spondylotic Myelopathy Correlated With Long-Term Functional Outcome

BACKGROUND Fractional anisotropy (FA) of the high cervical cord correlates with upper limb function in acute cervical cord injury. We investigated the correlation between preoperative FA at the level of maximal compression and functional recovery in a group of patients after decompressive surgery for cervical spondylotic myelopathy (CSM). OBJECTIVE To determine the usefulness of FA as a biomarker for severity of CSM and as a prognostic biomarker for improvement after surgery. METHODS Patients received diffusion tensor imaging (DTI) scans preoperatively. FA values of the whole cord cross-section at the level of maximal compression and upper cervical cord (C1-2) were calculated. Functional status was measured using the modified Japanese Orthopedic Association (mJOA) scale preoperatively and at follow-up up to 2 yr. Regression analysis between FA and mJOA was performed. DTI at C4-7 was obtained in controls. RESULTS Forty-four CSM patients enrolled prior to decompression were compared with 24 controls. FA at the level of maximal compression correlated positively with preoperative mJOA score. Preoperative FA correlated inversely with recovery throughout the postoperative period. This was statistically significant at 12 mo postoperation and nearly so at 6 and 24 mo. Patients with preoperative FA0.55. CONCLUSION In the largest longitudinal study of this kind, FA promises a valid biomarker for severity of CSM and postoperative improvement. FA is an objective measure of function and could provide a basis for prognosis. FA is particularly useful if preoperative values are less than 0.55