File Log Research Articles

Displacement Mapping as a Highly Flexible Surface Texturing Tool for Additively Photopolymerized Components.

Displacement mapping is a computer graphics technique that enables the design of components with regularly or randomly textured surfaces that can be quickly materialized on a three-dimensional (3D) printer when needed. This approach is, in principle, more flexible, faster, and more economical compared to conventional texturing methods, but the accuracy of the texture depends heavily on the parameters used. The purpose of this study is to demonstrate how to produce a surface-textured part using polygonal (mesh) modeling software and a photopolymerizable resin and to develop a universal methodology to predict the dimensional accuracy of the model file log combined with a resin 3D printer. The printed components were characterized on a scanning confocal microscope. In the setup used in this study, the mesh size had to be reduced to 10% of the smallest feature size, and the textured layer had to be heavily (×4.5) overexposed to achieve the desired accuracy. As a practical application, two functional stamps with a regular (honeycomb) and a random texture, respectively, were successfully manufactured. The insights gained will be of great benefit for quickly and cost-effectively producing components with innovative patterns and textures for a variety of hobby, industrial, and biomedical applications.

Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation.

Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour. Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case. There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p < 0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p < 0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05). The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies.

AN ENHANCED SOFTWARE AS A SERVICE (SAAS) ARCHITECTURAL MODEL FOR CLOUD BASED SECURITY USING HYBRID SYMMETRIC ALGORITHM

Protecting cloud data from security breaches, preventing and ensuring safe cloud data is concealed from unauthorized access or infiltration by unwanted users are all part of cloud infrastructure security. This proposed technique is based on ensuring data confidentiality by adding extra-layer of security from the client side, developing a framework that implements the AES 256-bit key and Fernet 128-bits algorithms, which encrypt and decrypt files via random algorithm selection during uploads and downloads. When a file is uploaded to the cloud, the decryption key is recorded in the local server's file log. The software use randomized algorithm selection to provide security to ensure that an attacker having a prior link cannot reset the user account since the attacker cannot guess the token. Furthermore, invalidating this token ensures that the link cannot be used more than once if it was previously logged anywhere. To propose and verify the efficiency of the model the results generated(in quantitative values) are frequency (monobit) testing, with a minimum bit size of 64, given the P-values derived from the above study (i.e., 0.8838, 0.6187, 0.3768, 0.2817, 0.5843, 0.4167). The findings reveal throughput for encryption of 0.91531 and for decryption of 0.4854, space complexity of 0.4854, and entropy of 7.942616667.This research work studies the design of a robust, adaptable, and non-deterministic SaaS application that can be secure for enterprises and subsequent research studies will be conducted in the development.

File Log Server and Application in Server System Privacy

The article research on file log server and application off file log into operation as well as server system privacy. From that, authorities had conducted log analysis system of which ability of updating data according to real time, helping the log serve analysing find to be easier and more intuitive than Kibana, system helps server privacy be easier when detecting errors and warning immidiately, recognizing abnormal signatures help administrator to receive recommendations in the earliest and most precise way. Comparing to handmade or following to troubles, this is absolutle an optimal choice.

Abstract 172: Early Impact of a Technology Solution to Transform Care for Cardiometabolic Conditions: Physician Experience and Patient-Centered Outcomes

Objectives: To assess early experience with a digital health solution introduced to primary care. Background: Managing cardiometabolic conditions (diabetes, hypertension, and dyslipidemia) is challenging for patients and physicians. Patients often lack the understanding and motivation to adopt healthy behaviors and adhere to treatment plans. Physicians face the challenge of having limited time to engage and educate patients at the point-of-care. Electronic health records (EHRs) have not solved this problem; in fact, reports suggest that EHRs increase administrative tasks, reducing time with patients and contributing to burnout. To address these issues we developed CM-SHARE, a web-based application that provides physicians and patients a dashboard of critical health information needed at the point of care, actionable cues to address guideline-based care and quality gaps, and visual tools to educate patients. Methods: CM-SHARE was designed with input from practicing primary care physicians (PCPs) and launched in two clinics in April 2016. Physician use of CM-SHARE is voluntary, and regular feedback is elicited for ongoing development. Both quantitative and qualitative measures assess the impact of CM-SHARE. Quantitative measures included use (e.g., number of launches, duration of use, clicks on specific features and functions) and measures of physician efficiency based on the EHR audit file logs. Semi-structured interviews were conducted with both physicians and patients to assess perceptions of CM-SHARE’s impact on physician-patient communication, patients’ knowledge of their conditions and motivations to change health behaviors, as well as value, barriers/facilitators of use, and desired improvements. A pre-post, matched control design was used to assess early experience and impact. Results: From April 27, 2016 to November 1, 2017, CM-SHARE was used in 7,900 encounters for 3,712 patients by 6 PCPs. CM-SHARE was launched in 30% of all encounters, a rate that nearly doubled (to 59%) if cardiometabolic health issues were addressed in the visit. Use of CM-SHARE was associated with a 25%-35% reduction in time spent in the EHR during an encounter and an 11%-20% reduction in the number of EHR clicks during the encounter. Both patients and physicians reported that CM-SHARE enhanced the patient-physician communication, particularly for patients for whom behavior change is necessary to improve clinical measures (e.g., A1c). Following this early experience, CM-SHARE adoption has expanded to 30+ providers, including physicians and an expanded user base of chronic disease case managers and diabetes educators. Conclusions: Digital health solutions such as CM--SHARE can be successfully integrated into routine care settings and favorably influence physician efficiency and patient experience. Further research will explore the impact of CM-SHARE on longer-term health outcomes.

A Proposal for Information Systems Security Monitoring Based on Large Datasets

This article describes how the objective of recent advances in soft computing and machine learning models is the resolution of issues related to security monitoring for information systems. Most current techniques and models face significant limitations, in the monitoring of information systems. To address these limitations, the authors propose a new model designed to detect potential security breaches at an early stage using logging data. The proposed model uses unsupervised training techniques with a rule-based system to analyse data file logs. The proposed approach has been evaluated using a case study based on the learning of data file logs to determine the effectiveness of the proposed approach. Experimental results show that the proposed approach performs well, the results demonstrate that the proposed approach performs better than other conventional security methods in the identification of the correct decisions related to potential security in information systems.

Learning analytics for the development of adapted e‐assessment workflow system

ABSTRACTLearning analytics (LA) is a significant area of the enhanced learning technology that has been emerged during the last decade. In this paper, we present a Cloud Adapted Workflow e‐Assessment System, called Cloud‐AWAS. This system makes use of the learning analytics in order to turn learners into more effective and better learners. Hence, Cloud‐AWAS could be seamlessly integrated into any learning management system. This system provides a generic e‐assessment workflow which is adapted to learner's profiles. We have started by creating a learner profile ontology based on extraction data from e‐assessment activities, file log and personal information. Then, we have defined three adaptation actions: Add Activity, Edit Activity, and Delete Activity, applied on the workflow assessment and using information extracted from learner profile ontology instances. Each action is applied according to a number of conditions. Finally, we present some results of the empirical evaluation of Cloud_AWAS. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:951–966, 2016; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21770

A Real-Time Log Analyzer Based on MongoDB

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.