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ردیف | عنوان | نوع |
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1 |
DOPIV: Post-Quantum Secure Identity-Based Data Outsourcing with Public Integrity Verification in Cloud Storage
DOPIV: برون سپاری داده مبتنی بر هویت امن پس از کوانتومی با تأیید صحت عمومی در فضای ذخیره سازی ابری-2022 Public verification enables cloud users to employ a third party auditor (TPA) to check the data integrity. However, recent
breakthrough results on quantum computers indicate that applying quantum computers in clouds would be realized. A majority of existing
public verification schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with
quantum computers in the near future. Moreover, new security issues need to be solved when an original data owner is restricted or
cannot access the remote cloud server flexibly. In this paper, we propose an efficient identity-based data outsourcing with public integrity
verification scheme (DOPIV) in cloud storage. DOPIV is designed on lattice-based cryptography, which achieves post-quantum security.
DOPIV enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server.
Any TPA can perform data integrity verification efficiently on behalf of the original data owner, without retrieving the entire data set.
Additionally, DOPIV possesses the advantages of being identity-based systems, avoiding complex certificate management procedures.
We provide security proofs of DOPIV in the random oracle model, and conduct a comprehensive performance evaluation to show that
DOPIV is more practical in post-quantum secure cloud storage systems.
Index Terms: Cloud storage | public verification | lattice-based cryptography | identity-based data outsourcing | post-quantum security |
مقاله انگلیسی |
2 |
Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022 Computer vision systems perform based on their design and parameter setting. In computer vision systems
that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of
the systems in terms of both results quality and computational costs. Appropriate setting of the weights for
the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision
system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or
no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the
grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the
system and subsystem to small changes in the distribution of data in a color space of the processed images.
We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the
advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show
that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies.
The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer
vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion |
مقاله انگلیسی |
3 |
Barriers to computer vision applications in pig production facilities
موانع برنامه های بینایی کامپیوتری در تاسیسات تولید خوک-2022 Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status
in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large,
commercial pig production systems and requires strong observational skills and a working knowledge of animal
husbandry and livestock systems operations. In recent years, many studies have explored the use of various
technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these
technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a
systematic review of the state of application of this technology indicates that there are many significant barriers
to its widespread adoption and successful utilization in commercial production system settings. One of the most
important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not
measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and
practical needs being instituted by scientific experts working in commercial animal production partnerships. This
lack of synergy between experts in the computer vision and animal health and production sectors means that
existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for
use in other contexts, resulting in a generality versus particularity conundrum.
This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective
use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following:
(i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of
computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset
construction for computer vision algorithm development. In this appraisal, we outline common difficulties and
challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig
management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to
applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies
and lead the computer vision technologies to commercial applications in pig production facilities.
keywords: بینایی کامپیوتر | دامپروری دقیق | رفتار - اخلاق | یادگیری عمیق | مجموعه داده | گراز | Computer vision | Precision livestock farming | Behavior | Deep learning | Dataset | Swine |
مقاله انگلیسی |
4 |
A turnaround control system to automatically detect and monitor the time stamps of ground service actions in airports: A deep learning and computer vision based approach
یک سیستم کنترل چرخش برای شناسایی و نظارت خودکار بر مهرهای زمانی اقدامات خدمات زمینی در فرودگاهها: یک رویکرد مبتنی بر یادگیری عمیق و بینایی کامپیوتری-2022 As it is widely known, several ground services are provided by the airports for the domestic and international
flights of the commercial passenger aircraft. Some of these services are conducted during the period called
as the turnaround which starts with the parking of the aircraft in the aprons before the flight and ends with
their leave from the aprons for the flight. Turnaround processes achieved in short time periods allow using the
limited airport resources including the service vehicles and staff effectively. In addition, commercial reputation
losses and financial losses that may arise from delays can be reduced as well as the delay-associated turnaround
penalties. In this article, a deep learning and computer vision based system that detects and allows monitoring
the airport service actions is proposed. The proposed system is capable of analyzing all the primary ground
services for an aircraft parking on its apron by employing the RGB video frame sequences obtained from a
single fixed camera focusing on the apron. In the service detection and analysis modules of the proposed airport
ground service analysis system, some deep learning-based subsystems and in-house-developed algorithms were
included and utilized. For the training of the machine learning models, a study-specific dataset was used and
the constructed learning models were evaluated on real-life cases. Experimental results obtained as a result of
the performance evaluations show that the proposed system is quite successful with precision rates over 90%
in the detection and analysis of the airport ground services. This study is one of the limited research studies
in which deep learning and computer vision techniques have been applied to detect and analyze the ground
service actions. The proposed system is also capable of real-time data processing/analysis and concurrent
service action monitoring. Furthermore, it allows monitoring when the service is received by stamping the
times of service start/end. In a consideration of industrial relevance or operational perspective, such a system
may facilitate the airport ground service management noticeably and reduce the delay-associated costs caused
by the timing of the ground services.
keywords: سیستم کنترل گردش فرودگاه | نظارت بر حرکت چرخشی | شناسایی وسایل نقلیه فرودگاهی | تشخیص چرخش | خدمات فرودگاهی | Airportturnaroundcontrolsystem | Turnaroundactionmonitoring | Airportvehicledetection | Turnaroundactiondetection | Airportgroundservices |
مقاله انگلیسی |
5 |
Performance evaluation of Focused Beam Routing for IoT applications in underwater environment
ارزیابی عملکرد مسیریابی پرتو متمرکز برای کاربردهای اینترنت اشیا در محیط زیر آب-2022 Underwater applications are becoming more and more interesting to industry and academy.
They include data gathering for human safety and environment monitoring, control of underwater robots for various tasks and so on. Because of the accessibility limitations in underwater
environment, applications tend to be automated and delay tolerant. In this paper, we consider
IoT applications in underwater environment, while using Delay Tolerant Networking (DTN)
carry–store–forwarding paradigm. DTN routing protocols are used to forward data from the
monitoring mobile sensors to collecting devices at the water surface and vice-versa. One
characteristic of routing protocols for DTN is flooding of messages to increase the delivery
probability. For instance, Epidemic Routing (ER) protocol creates a copy of each message for
each new node that does not already have the message in its memory. This increases the
probability of delivery, but on the other hand, creates overhead in each node’s buffer, and uses
a lot of valuable energy from the forwarding and receiving nodes. This work aims to analyze by
simulations the performance of Focused Beam Routing (FBR) protocol for different FBR angles
and different applications. We use Delivery Probability, Average Number of Hops, Overhead
Ratio and Buffer Occupancy to simulate our scenarios by The ONE simulator. Simulation results
show that for narrow angles of FBR the performance is better. In case of FBR-45, average
hop count and overhead ratio are decreased by 10.9% and 16.6% respectively, compared to
FBR-180. However, delivery probability decreases by only 3.9%.
Keywords: Underwater environment | Delay tolerant network | DTN | Focused Beam Routing | FBR the ONE simulator |
مقاله انگلیسی |
6 |
Publish–Subscribe approaches for the IoT and the cloud: Functional and performance evaluation of open-source systems
رویکردهای انتشار – اشتراک برای اینترنت اشیا و ابر: ارزیابی عملکرد و کارایی سیستمهای منبع باز-2022 Publish–Subscribe systems facilitate the communication between services or applications. A
typical system comprises the publisher, the subscriber, and the broker but, may also feature
message queues, databases, clusters, or federations of brokers, apply message delivery policies,
communication protocols, security services, and a streaming API. Not all these features are
supported by all systems or, others may be optional. As a result, there is no common ground
for the comparison of Publish–Subscribe systems. This paper presents a critical survey and
taxonomy of Publish–Subscribe systems, of their design features and technologies. The concepts
of message queuing, publish–subscribe systems, and publish–subscribe protocols for the cloud
and the IoT are discussed and clarified. The respective evaluation is about seven state-of-the-art
open-source systems namely, Apache Kafka, RabbitMQ, Orion-LD, Scorpio, Stellio, Pushpin, and
Faye. For the sake of fair comparison, a minimum set of common features is identified in all
systems. All systems are evaluated and compared in terms of functionality and performance
under real-case scenarios.
keywords: صف پیام | انتشار – اشتراک | معیارها | ارزیابی | Message queue | Publish–subscribe | Benchmarks | Evaluation |
مقاله انگلیسی |
7 |
A flexible Compilation-as-a-Service and Remote-Programming-as-a-Service platform for IoT devices
یک پلت فرم انعطاف پذیر مجموعه به عنوان سرویس و برنامه نویسی راه دور به عنوان سرویس برای دستگاه های اینترنت اشیا-2022 The Internet-of-Things (IoT) presents itself as an emerging technology, which is able to interconnect a massive number of heterogeneous smart objects. Several complex data-driven applications, such as smart cities applications, home automation, health monitoring, etc., have been
realized through the existence of these ubiquitous networks of smart objects. The ability to remotely update the devices forming an IoT network is of paramount importance, as it enables
adding new functionality in their firmware, either for resolving software bugs and security vulnerabilities or for application re-purposing, without the need to physically access them. In this
work, we present a flexible Compilation-as-a-Service and Remote-Programming-as-a-Service
platform that jointly offers cloud-based compilation and Firmware-Over-The-Air (FOTA) update
functionalities for deployed IoT devices, in a reliable and secure manner. Our system is capable
of easily supporting various embedded operating systems and heterogeneous hardware platforms.
We describe the system architecture and elaborate on the implementation details of all system
components. In addition, we perform an extensive performance evaluation of a Proof-of-Concept
(PoC) deployment of our system and discuss results in terms of system response, scalability and
resource utilization.
keywords: Internet-of-Things | Cloud computing | Platform-as-a-Service | Cloud compilation | Over-the-air programming |
مقاله انگلیسی |
8 |
Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستمهای تولید هوشمند با استفاده از تجزیه و تحلیل پیشبینیکننده-2022 Smart manufacturing systems (SMS) are one of the most important applications in the Industry
4.0 era, offering numerous advantages over traditional production systems and rapidly being
used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT),
Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more.
SMS is an innovative and popular manufacturing setup that produces increasingly intelligent
production systems; yet, designers must adapt to business tastes and requirements. This study
employs an analytical and descriptive research technique to identify and assess functional and
non-functional, technological, economic, social, and performance evaluation components that
are essential to SMS evaluation. A predictive analytics framework, which is a key component
of many decision support systems, is used to assess corporate needs as well as proposed and
prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system |
مقاله انگلیسی |
9 |
A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN
تشخیص شخصی نوار قلب ECG مبتنی بر وابستگی های عملکردی و ساختاری سیگنالها با استفاده از نمایش فرکانس زمان و CNN مورفولوژیکی تکاملی-2021 Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them. Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks |
مقاله انگلیسی |
10 |
Lossless fuzzy extractor enabled secure authentication using low entropy noisy sources
استخراج کننده فازی بدون تلفات ، احراز هویت ایمن را با استفاده از منابع پر سر و صدا کم آنتروپی فعال کرد-2021 Fuzzy extractor provides a way for key generation from biometrics and other noisy data. It has been widely applied in biometric authentication systems that provides natural and passwordless user authentication. In general, given a random sample, a fuzzy extractor extracts a nearly uniform random string, and subsequently regenerates the string using a different yet similar noisy sample. However, due to error tolerance between the two samples, fuzzy extractor imposes high information loss (entropy) and thus, it only works for an input with high enough entropy. In this work, we propose a lossless fuzzy extractor for a large family of sources. The proposed lossless fuzzy extractor can be adopted for a wider range of random sources to extract an arbitrary number of nearly uniform random strings. Besides, we formally defined a new entropy measurement, named as equal error entropy, to measure the entropy loss in reproducing a bounded number of random strings. When the number of random strings is large enough, the equal error entropy is minimized and necessary for performance evaluation on the authentication using the extracted random strings. Keywords: Authentication | Biometric | Fuzzy extractor | Secure sketch |
مقاله انگلیسی |