Integrating Security with Accuracy Evaluation in Sensors Fusion

This paper presents a novel approach of integrating sensor accuracy and sensor platform security in overall sensor system evaluation and presents a new developed method and a software tool that are designed to assist the sensor community in assessing the security aspect of complex sensor platforms such as smartphones. We recommend conducting sensor platform security evaluation along with the accuracy estimation and employing them together while designing sensor fusion. We developed a mobile platform\u27s security evaluation tool, which we describe in this paper, and encourage its application in this process. Also, we describe a use case that shows how sensors fusion may affect not only sensor accuracy but also data security in practice. By introducing security into sensor system design at the earlier stage, we try to bridge sensor accuracy and security evaluation

Influence of Transfer Learning on Machine Learning Systems Robustness to Data Quality Degradation

The evolution of the Machine Learning (ML) has led to the emergence of Transfer Learning (TL) approach, which allows reusing pretrained industrial ML systems after their input data values or even an application domain has changed. In this paper, we investigate the TL process and its impact on the performance of ML-end systems with integrated network facilities. Especially, we focus on ML-systems designed for the classification of image media-files, transmitted over a network. Packet loss in a network is considered as a major input Data Quality (DQ) deterioration factor that can result in ML system classification performance degradation after pretraining on good inputs. To investigate the typical industrial TL process, we study the relationships between the ML model\u27s last layer weights, hyperparameters, and classification performance throughout the retraining process. In addition, we conduct an empirical study to evaluate how the TL affects ML model performance in real application scenarios. For our experiments, we employ real image media-files, and transmit them over a real wireless network with inherent data losses for a classification on a remote ML-end system. According to our results, retraining ML models on corrupted data allows to enhance their robustness to a DQ degradation in the considered image classification scenarios. However, DQ influence on the ML system performance may vary depending on the data and system types

Multi-Modal Sensor Selection with Genetic Algorithms

We develop Genetic Algorithms based method and the tool to select sensors, which provide the specified quality of data after fusion. In this paper, we concentrate on introducing multi-modal sensor fusion in the selection operation. To evaluate data quality, we consider the combination of diverse sensor\u27s accuracy and security metrics. We modify data quality evaluation calculus that incorporates these major metrics to include the possibility of multi-modal sensor fusion. To evaluate Genetic Algorithm feasibility in sensor selection, we compare it against the conventional brute force search approach. To implement our approach and facilitate its use in practice, we produce and release an Android application that automatically selects multi-modal sensors based on the specified sensor types and required data quality

Improving Knowledge Based Detection of Soft Attacks Against Autonomous Vehicles with Reputation, Trust and Data Quality Service Models

Autonomous vehicles group’s security and safety improvement and assurance is a challenging research problem. In this paper, we describe our smart data-oriented security service, which is aimed at detecting malfunctioning or malicious agents based on the fusion of multi-agents Reputation, Trust and Data Quality (DQ) models for traffic control. To address the classical Reputation zero value challenge, we introduce the DQ evaluation service, which allows to use the vehicle’s objective characteristics to assign the initial Reputation value to a new agent when it is joining the group. To validate our approach, we conducted an empirical study on real intersection traffic with multiple vehicles. Multiple experiments were performed on our custom physical intersection management test ground and even bigger vehicles groups were studied by simulation. The experimental results verify our approach capability to effectively detect malfunctioning and malicious agents. The empirical study confirmed that the DQ service improves detection performance

Mechanoactivated Refractory Compositions Based on Aluminum Phosphate Bonds for Melting Pots

В статье приведен анализ эксплуатационных свойств набивных тигельных масс в зависимости от их состава, активности компонентов и состава применяемых алюмофосфатных связующих (АФС). Найдены оптимальные режимы механоактивации графитов, а также соотношение активированных компонентов к неактивированным, позволяющее достичь показателя прочности при сохранении термостойкости композиции. Детально исследованы такие свойства, как работа адгезии, плотность, седиментационная устойчивость АФС. Установлено, что с уменьшением в составе АФС содержания гидроксида алюминия повышается кислотность и уменьшается седиментационная устойчивость. Показана зависимость прочности лабораторных образцов тиглей от температуры обжига и состава связующего.The paper analyzes the operational properties of stuffed melting pot masses, depending on their composition, activity of the components and used composition of the aluminum phosphate bonds (APB). Found optimal regimes of mechanical activation graphite, and also found the ratio of activated components to non-activated, allowing to achieve performance of strength when saving thermal stability of the composition. Studied in detail properties such as the work of adhesion, density, sedimentation stability of APB. It is established that a decrease in the content APB of aluminum hydroxide, increased acidity and decreased sedimentation stability. Shows the dependence of the strength of laboratory samples of melting pots from burning temperature and the composition of the binder

Mechanoactivated Refractory Compositions Based on Aluminum Phosphate Bonds for Melting Pots

В статье приведен анализ эксплуатационных свойств набивных тигельных масс в зависимости от их состава, активности компонентов и состава применяемых алюмофосфатных связующих (АФС). Найдены оптимальные режимы механоактивации графитов, а также соотношение активированных компонентов к неактивированным, позволяющее достичь показателя прочности при сохранении термостойкости композиции. Детально исследованы такие свойства, как работа адгезии, плотность, седиментационная устойчивость АФС. Установлено, что с уменьшением в составе АФС содержания гидроксида алюминия повышается кислотность и уменьшается седиментационная устойчивость. Показана зависимость прочности лабораторных образцов тиглей от температуры обжига и состава связующего.The paper analyzes the operational properties of stuffed melting pot masses, depending on their composition, activity of the components and used composition of the aluminum phosphate bonds (APB). Found optimal regimes of mechanical activation graphite, and also found the ratio of activated components to non-activated, allowing to achieve performance of strength when saving thermal stability of the composition. Studied in detail properties such as the work of adhesion, density, sedimentation stability of APB. It is established that a decrease in the content APB of aluminum hydroxide, increased acidity and decreased sedimentation stability. Shows the dependence of the strength of laboratory samples of melting pots from burning temperature and the composition of the binder

Metal-Graphite Sintered Composites with Using Mechanically Activated Materials

Проведены исследования по созданию недорогой металлографитовой вставки для пресс- матрицы, применяемой в процессе совмещенного литья и прокатки прессования. Установлено, что механоактивация исходных материалов улучшает спекаемость образцов и повышает их физико-механические свойства (прочность и твердость). В процессе исследования были разработаны составы, обеспечивающие высокие эксплуатационные свойства металлографитовых вставок. Разработаны технологические режимы подготовки и спекания материалов, позволяющие сократить время технологического процесса на 40-50 % и не требующие создания специальной защитной атмосферыThe studies of the creation of low-cost metal-graphite inserts press-matrix used in the combined casting and rolling compaction are made. It`s founded that the mechanoactivation of starting materials improves sinterability of samples and increases their mechanical properties (strength and hardness). Being the study formulations provide high operational properties metal-graphite inserts was designed. The technological modes of preparation and sintering materials that reduce process time by 40-50 % and do not require the creation of special protective atmosphere are develope

Metal-Graphite Sintered Composites with Using Mechanically Activated Materials

Проведены исследования по созданию недорогой металлографитовой вставки для пресс- матрицы, применяемой в процессе совмещенного литья и прокатки прессования. Установлено, что механоактивация исходных материалов улучшает спекаемость образцов и повышает их физико-механические свойства (прочность и твердость). В процессе исследования были разработаны составы, обеспечивающие высокие эксплуатационные свойства металлографитовых вставок. Разработаны технологические режимы подготовки и спекания материалов, позволяющие сократить время технологического процесса на 40-50 % и не требующие создания специальной защитной атмосферыThe studies of the creation of low-cost metal-graphite inserts press-matrix used in the combined casting and rolling compaction are made. It`s founded that the mechanoactivation of starting materials improves sinterability of samples and increases their mechanical properties (strength and hardness). Being the study formulations provide high operational properties metal-graphite inserts was designed. The technological modes of preparation and sintering materials that reduce process time by 40-50 % and do not require the creation of special protective atmosphere are develope

Fully-online, interoperable clinical trial management system for multi-interventional RCT : Maintain Your Brain digital platform

Maintain Your Brain (MYB)i is a randomised controlled trial (RCT) of multiple online interventions designed to target modifiable risk factors for Alzheimer's disease and dementia. Traditional clinical trial management systems (CTMS) requirements consist of features such as management of the study, site, subject (participant), clinical outcomes, external and internal requests, education, data extraction and reporting, security, and privacy. In addition to fulfilling these traditional requirements, MYB has a specific set of features that needs to be fulfilled. These specific requirements include: (i) support for multiple interventions within a study, (ii) flexible interoperability options with third-party software providers, (iii) study participants being able to engage in online activities via web-based interfaces throughout the trial (from screening to follow-up), (iv) ability to algorithmically personalize trial activities based on the needs of the participant, and (v) the ability to handle large volumes of data over a long period. This paper outlines how the existing CTMSs fall short in meeting these specific requirements. The presented system architecture, development approach and lessons learned in the implementation of the MYB digital platform will inform researchers attempting to implement CTMSs for trials comparable to MYB in the future

Maintain Your Brain: Protocol of a 3-Year Randomized Controlled Trial of a Personalized Multi-Modal Digital Health Intervention to Prevent Cognitive Decline Among Community Dwelling 55 to 77 Year Olds

Background: Maintain Your Brain (MYB) is a randomized controlled trial of an online multi-modal lifestyle intervention targeting modifiable dementia risk factors with its primary aim being to reduce cognitive decline in an older age cohort. Methods: MYB aims to recruit 8,500 non-demented community dwelling 55 to 77 year olds from the Sax Institute’s 45 and Up Study in New South Wales, Australia. Participants will be screened for risk factors related to four modules that comprise the MYB intervention: physical activity, nutrition, mental health, and cognitive training. Targeting risk factors will enable interventions to be personalized so that participants receive the most appropriate modules. MYB will run for three years and up to four modules will be delivered sequentially each quarter during year one. Upon completing a module, participants will continue to receive less frequent booster activities for their eligible modules (except for the mental health module) until the end of the trial. Discussion: MYB will be the largest internet-based trial to attempt to prevent cognitive decline and potentially dementia. If successful, MYB will provide a model for not just effective intervention among older adults, but an intervention that is scalable for broad use