MISS-D: A fast and scalable framework of medical image storage service based on distributed file system
MISS-D: یک چارچوب سریع و مقیاس پذیر از خدمات ذخیره سازی تصویر پزشکی بر اساس سیستم فایل توزیع شده-2020
Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging
Accelerated Computer Vision Inference with AI on the Edge
استنتاج چشم انداز رایانه ای سریع با هوش مصنوعی در لبه-2020
Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. The goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.
Index Terms: Artificial Intelligence | Deep Learning | Neural Networks | Computer Vision | AI on the Edge | OpenVINO
Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach
منافع شخصی و محافظت از داده ها باعث پذیرش اخلاقی و تطبیقی فناوری های داده بزرگ می شوند: یک رویکرد تجزیه و تحلیل مشترک-2020
Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N ¼ 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies. We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by selfinterest and do place importance on potential privacy violations. The results also challenge the assumption that people generally prefer the status quo.
Keywords: Moral acceptability | Big data | Conjoint analysis | Outcome favorability | Data protection | Data sharing
Managing complex engineering projects: What can we learn from the evolving digital footprint?
مدیریت پروژه های پیچیده مهندسی: از ردپای دیجیتال در حال تحول چه می توانیم یاد بگیریم؟-2020
The challenges of managing large complex engineering projects, such as those involving the design of infrastructure, aerospace and industrial systems; are widely acknowledged. While there exists a mature set of project management tools and methods, many of todays projects overrun in terms of both time and cost. Existing literature attributes these overruns to factors such as: unforeseen dependencies, a lack of understanding, late changes, poor communication, limited resource availability (inc. personnel), incomplete data and aspects of culture and planning. Fundamental to overcoming these factors belies the challenge of how management information relating to them can be provided, and done so in a cost eff ;ective manner. Motivated by this challenge, recent research has demonstrated how management information can be automatically generated from the evolving digital footprint of an engineering project, which encompasses a broad range of data types and sources. In contrast to existing work that reports the generation, verification and application of methods for generating management information, this paper reviews all the reported methods to appraise the scope of management information that can be automatically generated from the digital footprint. In so doing, the paper presents a reference model for the generation of managerial information from the digital footprint, an appraisal of 27 methods, and a critical reflection of the scope and generalisability of data-driven project management methods. Key findings from the appraisal include the role of email in providing insights into potential issues, the role of computer models in automatically eliciting process and product dependencies, and the role of project documentation in assessing project norms. The critical reflection also raises issues such as privacy, highlights the enabling technologies, and presents opportunities for new Business Intelligence tools that are based on real-time monitoring and analysis of digital footprints.
Keywords: Big Data | Project Management | Business Intelligence | Knowledge Workers
IP Addresses in the Context of Digital Evidence in the Criminal and Civil Case Law of the Slovak Republic
آدرسهای IP در زمینه شواهد دیجیتالی در پرونده کیفری و مدنی جمهوری اسلواکی-2020
Use of IP addresses by courts in their decisions is one of the issues with growing importance. This applies especially at the time of the increased use of the internet as a mean to violate legal provisions of both civil and criminal law. This paper focuses predominantly on two issues: (1) the use of IP addresses as digital evidence in criminal and civil proceedings and possible mistakes in courts approach to this specific evidence, and (2) the anonymisation of IP addresses in cases when IP addresses are to be considered as personal data. This paper analyses the relevant judicial decisions of the Slovak Republic spanning the time period from 2008 to 2019, in which the relevant courts used the IP address as evidence. On this basis, the authors formulate their conclusions on the current state and developing trends in the use of digital evidence in judicial proceedings. The authors demonstrate the common errors that occur in the courts’ decisions as regards the use of IP addresses as evidence in the cases of the IP addresses anonymisation, usage of the in dubio pro reo principle in criminal proceedings, and the relationship between IP addresses and devices and persons.
Keywords: IP address | Digital evidence | Criminal and civil proceedings | Privacy | Personal data | Anonymisation
AI Crimes: A Classification
جرایم هوش مصنوعی: طبقه بندی-2020
Intelligent and machine learning systems have infiltrated cyber-physical systems and smart cities with technologies such as internet of things, image processing, robotics, speech recognition, self-driving, and predictive maintenance. To gain user trust, such systems must be transparent and explainable. Regulations are required to control crimes associated with these technologies. Such regulations and legislations depend on the severity of the artificial intelligence (AI) crimes subject to these regulations, and on whether humans and/or intelligent systems are responsible for committing such crimes, and therefore can benefit from a classification tree of AI crimes. The aim of this paper to review prior work in ethics for AI, and classify AI crimes by producing a classification tree to assist in AI crime investigation and regulation.
Keywords: AI | classification tree | crimes | ethics | explainable AI | transparency | trust | privacy
A taxonomy of AI techniques for 6G communication networks
طبقه بندی تکنیک های هوش مصنوعی برای شبکه های ارتباطی 6G-2020
With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today’s 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.
Keywords: Artificial Intelligence | 6G | Communication networks | Mobile edge computing | Intelligent transportation system
Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments
به سمت امنیت و حفظ حریم خصوصی برای هوش مصنوعی لبه در محیط های بازاریابی دیجیتال مبتنی بر IoT / IoE-2020
Abstract—Edge Artificial Intelligence (Edge AI) is a crucial aspect of the current and futuristic digital marketing Internet of Things (IoT) / Internet of Everything (IoE) environment. Consumers often provide data to marketers which is used to enhance services and provide a personalized customer experience (CX). However, use, storage and processing of data has been a key concern. Edge computing can enhance security and privacy which has been said to raise the current state of the art in these areas. For example, when certain processing of data can be done local to where requested, security and privacy can be enhanced. However, Edge AI in such an environment can be prone to its own security and privacy considerations, especially in the digital marketing context where personal data is involved. An ongoing challenge is maintaining security in such context and meeting various legal privacy requirements as they themselves continue to evolve, and many of which are not entirely clear from the technical perspective. This paper navigates some key security and privacy issues for Edge AI in IoT/IoE digital marketing environments along with some possible mitigations.
Keywords: edge security | edge privacy | edge AI | edge intelligence | artificial intelligence | AI | machine learning | ML | IoT | IoE | edge | cybersecurity | legal | law | digital marketing | smart | GDPR | CCPA | security | privacy
Attacking and defending multiple valuable secrets in a big data world
حمله و دفاع از اسرار چند ارزشمندی در جهان داده های بزرگ-2020
This paper studies the attack-and-defence game between a web user and a whole set of players over this user’s ‘valuable secrets.’ The number and type of these valuable secrets are the user’s private information. Attempts to tap information as well as privacy protection are costly. The multiplicity of secrets is of strategic value for the holders of these secrets. Users with few secrets keep their secrets private with some probability, even though they do not protect them. Users with many secrets protect their secrets at a cost that is smaller than the value of the secrets protected. The analysis also accounts for multiple redundant information channels with cost asymmetries, relating the analysis to attack-and-defence games with a weakest link.
Keywords: Big-data | Privacy | Conflict | Valuable secrets | Attack-and-defence
Hiding Private Information in Images From AI
پنهان کردن اطلاعات خصوصی در تصاویر از هوش مصنوعی-2020
Privacy protection attracts increasing concerns these days. People tend to believe that large social platforms will comply with the agreement to protect their privacy. However, photos uploaded by people are usually not treated to achieve privacy protection. For example, Facebook, the world’s largest social platform, was found leaking photos of millions of users to commercial organizations for big data analytics. A common analytical tool used by these commercial organizations is the Deep Neural Network (DNN). Today’s DNN can accurately identify people’s appearance, body shape, hobbies and even more sensitive personal information, such as addresses, phone numbers, emails, bank cards and so on. To enable people to enjoy sharing photos without worrying about their privacy, we propose an algorithm that allows users to selectively protect their privacy while preserving the contextual information contained in images. The results show that the proposed algorithm can select and perturb private objects to be protected among multiple optional objects so that the DNN can only identify non-private objects in images.
Index Terms: privacy | object detection | deep learning