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ردیف | عنوان | نوع |
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1 |
Efficient Implementation of Lightweight Hash Functions on GPU and Quantum Computers for IoT Applications
اجرای کارآمد توابع هش سبک در GPU و کامپیوترهای کوانتومی برای کاربردهای اینترنت اشیا-2022 Secure communication is important for Internet of Things (IoT) applications, to avoid cybersecurity attacks. One of the key security aspects is data integrity, which can be protected by employing cryptographic hash functions. Recently, US National Institute of Standards and Technology (NIST)
announced a competition to standardize lightweight hash functions, which can be used in IoT applications.
IoT communication involves various hardware platforms, from low-end microcontrollers to high-end cloud
servers with GPU accelerators. Since many sensor nodes are connected to the gateway devices and cloud
servers, performing high throughput integrity check is important to secure IoT applications. However, this is a
time consuming task even for high-end servers, which may affect the response time in IoT systems. Moreover,
no prior work had evaluated the performance of NIST candidates on contemporary processors like GPU and
quantum computers. In this study, we showed that with carefully crafted implementation techniques, all
the finalist hash function candidates in the NIST standardization competition can achieve high throughput
(up-to 1,000 Gbps) on a RTX 3080 GPU. This research output can be used by IoT gateway devices and cloud
servers to perform data integrity checks at high speed, thus ensuring a timely response. In addition, this is
also the first study that showcase the implementation of NIST lightweight hash functions on a quantum
computer (ProjectQ). Besides securing the communication in IoT, these efficient implementations on a GPU
and quantum computer can be used to evaluate the strength of respective hash functions against brute-force
attack.
INDEX TERMS: Graphics processing units (GPU) | hash function | lightweight cryptography | quantum computer. |
مقاله انگلیسی |
2 |
ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022 Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to
detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive,
manual task. This study proposes a novel approach for automated plumage condition assessment using com-
puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects
hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of
input image characteristics, the method was evaluated using images with and without depth information in
resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of
subjective human annotations, plumage condition predictions were compared to manual assessments of one
observer and to matching annotations of two observers. Among all tested settings, performance metrics based on
matching manual annotations of two observers were equal or better than the ones based on annotations of a
single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of
98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it
was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not
generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a
resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using
lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of
plumage conditions in commercial laying hen farms. keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation |
مقاله انگلیسی |
3 |
On the Capacity of Quantum Private Information Retrieval From MDS-Coded and Colluding Servers
در مورد ظرفیت بازیابی اطلاعات خصوصی کوانتومی از سرورهای کدگذاری شده و تبانی MDS-2022 In quantum private information retrieval (QPIR),
a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The
QPIR capacity is the maximal achievable ratio of the retrieved file
size to the total download size. In this paper, the capacity of QPIR
from MDS-coded and colluding servers is studied for the first
time. Two general classes of QPIR, called stabilizer QPIR and
dimension-squared QPIR induced from classical strongly linear
PIR are defined, and the related QPIR capacities are derived.
For the non-colluding case, the general QPIR capacity is derived
when the number of files goes to infinity. A general statement on
the converse bound for QPIR with coded and colluding servers
is derived showing that the capacities of stabilizer QPIR and
dimension-squared QPIR induced from any class of PIR are
upper bounded by twice the classical capacity of the respective
PIR class. The proposed capacity-achieving scheme combines the
star-product scheme by Freij-Hollanti et al. and the stabilizer
QPIR scheme by Song et al. by employing (weakly) self-dual
Reed–Solomon codes.
Index Terms: Private information retrieval (PIR) | information theoretic privacy | quantum information theory | capacity. |
مقاله انگلیسی |
4 |
Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning
مدیریت منابع برای IoV به کمک هوش لبه (EI) با استفاده از یادگیری تقویتی الهام گرفته از پردازش کوانتومی-2022 Recent developments in the Internet of Vehicles
(IoV) enable interconnected vehicles to support ubiquitous
services. Various emerging service applications are promising to
increase the Quality of Experience (QoE) of users. On-board
computation tasks generated by these applications have heavily
overloaded the resource-constrained vehicles, forcing it to offload
on-board tasks to other edge intelligence (EI)-assisted servers.
However, excessive task offloading can lead to severe competition
for communication and computation resources among vehicles,
thereby increasing the processing latency, energy consumption,
and system cost. To address these problems, we investigate
the transmission-awareness and computing-sense uplink resource
management problem and formulate it as a time-varying Markov
decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL)
is proposed to develop an intelligence-oriented edge offloading
strategy. Specifically, the vehicle can flexibly choose the network
access mode and offloading strategy through two different radio
interfaces to offload tasks to multiaccess edge computing (MEC)
servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive
balance between these two aspects. Simulation results show that
the proposed algorithm can significantly reduce the transmission
latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL) |
مقاله انگلیسی |
5 |
AI for next generation computing: Emerging trends and future directions
هوش مصنوعی برای محاسبات نسل بعدی: روندهای نوظهور و مسیرهای آینده-2022 Autonomic computing investigates how systems can achieve (user) specified ‘‘control’’ outcomes on their own, without the intervention of a human operator. Autonomic computing
fundamentals have been substantially influenced by those of control theory for closed and
open-loop systems. In practice, complex systems may exhibit a number of concurrent and
inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g.,
multiple resources within a data centre), research into integrating Artificial Intelligence (AI)
and Machine Learning (ML) to improve resource autonomy and performance at scale continues
to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and
self-management of systems can be achieved at different levels of granularity, from full to
human-in-the-loop automation. In this article, leading academics, researchers, practitioners,
engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing
join to discuss current research and potential future directions for these fields. Further, we
discuss challenges and opportunities for leveraging AI and ML in next generation computing for
emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing
environments.
Keywords: Next generation computing | Artificial intelligence | Cloud computing | Fog computing | Edge computing | Serverless computing | Quantum computing | Machine learning |
مقاله انگلیسی |
6 |
A Survey on Post-Quantum Public-Key Signature Schemes for Secure Vehicular Communications
مرور طرحهای امضای کلید عمومی پسا کوانتومی برای ارتباطات امن خودرو-2022 Basic security requirements such as confidentiality,
user authentication and data integrity, are assured by using
public-key cryptography (PKC). In particular, public-key signature schemes provide non-repudiation, integrity of transmitted
messages and authentication. The presence of a large scale
quantum computer would be a real threat to break the most
widely used public-key cryptographic algorithms in practice,
RSA, DSA, ECDSA signature schemes and Diffie-Hellman key
exchange. Thus, all security protocols and applications where
these public-key cryptographic algorithms are used are vulnerable to quantum-computer attacks. There are five directions of
cryptographic primitives secure against a quantum computer:
multivariate quadratic equation-based, hash-based, lattice-based,
code-based and supersingular isogeny-based cryptography. These
primitives could serve as replacements for current public-key
cryptographic algorithms to prepare for post-quantum era. It is
important to prioritize the fields to be replaced by post-quantum
cryptography (PQC) since it is hard to replace the currently
deployed PKC with PQC at the same time. The fields directly
connected to human life such as vehicular communications should
be the primary targets of PQC applications. This survey is
dedicated to providing guidelines for adapting the most suitable
post-quantum candidates to the requirements of various devices
and suggesting efficient and physically secure implementations
that can be built into existing embedded applications as easily
as traditional PKC. It focuses on the five types of post-quantum
signature schemes and investigates their theoretical backgrounds,
structures, state-of-the-art constructions and implementation
aspects on various platforms raging from resource constrained
IoT devices to powerful servers connected to the devices for
secure communications in post-quantum era. It offers appropriate solutions to find tradeoffs between key sizes, signature
lengths, performance, and security for practical applications.
Index Terms— Implementation attack | post-quantum cryptography | public-key signature scheme | quantum algorithm | Shor algorithm | side-channel attack. |
مقاله انگلیسی |
7 |
Analysis and enhancement of secure three-factor user authentication using Chebyshev Chaotic Map
تجزیه و تحلیل و افزایش احراز هویت کاربر سه عامل امن با استفاده از نقشه آشفته Chebyshev-2021 The most popular solution for a variety of business applications such as e-banking and e-healthcare is the multi-server environment. The user registration of individual servers is not the primary concern in such an environment. Here, the user can get various services from different servers by registering him/her under one server. To get secure services through this environment, the authenticity of users and servers are crucial. In this observation, the smart card based biometric authentication system is well popular and easy to use. This paper delineates that the security flaws can be found in a trusted authentication scheme proposed by Chatterjee et al. (2018). Besides, this work proposes an enhanced authentication scheme namely, asymmetric encryption based secure user authentication (ASESUA) to eliminate the drawbacks of Chatterjee et al.’s scheme. The formal security analysis of ASESUA has been done with the help of random oracle model and verified by the well popular AVISPA tool. The analysis shows that ASESUA performs better concerning security, communication cost, and computation cost than other related existing schemes. Keywords: Attack | Authentication | Biometric | Smart card | Security |
مقاله انگلیسی |
8 |
Research on prepaid account financing model based on embedded system and Internet of Things
تحقیق در مورد مدل تامین مالی پیش پرداخت بر اساس سیستم جاسازی شده و اینترنت اشیا-2021 Internet of Things (IoT) network interconnection to create objects and things will play the Internet to play an
active role in the global network in the future. For the Internet of Things, which is widely adopted through
funding models, it must be trusted in the IoT security infrastructure. Efficiently and Securely IoT is very
important to define how each other can communicate with remote servers and get Exchange account informa-
tion. Prepayments for effective financial management and an important choice for financial IoT for service
providers and customers. However, it must be supported by real-time credit checking and costing. Internet re-
sources are consumed by these real-time action stuff providers and impose high costs on the old system. To solve
this problem, to propose the K Means Algorithm scalable accounting solutions, where the user is hosted each
occupies a prepaid account, constitute the components of embedded systems. Based on each of our prepaid
billing components’ supervision, it is at the same time consumed by the embedded system of all services, based
on the calculation of the service packages consumed by the customer. Prepaid accounts are reassigned when the
customer had sufficient credit to supplement their use and are allocated based on IoT services’ consumption. This
work aims to reduce the cost of pre-paid services and ensure that service delivery is not to interfere with the
charging unit. Also, embedded systems’ theoretical and experimental analysis shows that this work can store
long-lived services on the Internet of Things to provide inexpensive accounting solutions.
keywords: الگوریتم میانگین کا | سیستم های جاسازی شده | اینترنت اشیا | مدیریت مالی | سیستم حسابداری پیش پرداخت | K means algorithm | Embedded systems | Internet of Things | Financial management | Prepaid accounting system |
مقاله انگلیسی |
9 |
Multimodal biometric authentication for mobile edge computing
Multimodal biometric authentication for mobile edge computing-2021 In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) sys- tem designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing.© 2021 Elsevier Inc. All rights reserved. Keywords: Privacy Preserving Biometric Authentication (PPBA) | Mobile Edge Computing (MEC) | Multimodal Biometrics | Hill Climbing Attacks (HCA) | Euclidean distance | Malicious security |
مقاله انگلیسی |
10 |
A Prenatal Ultrasound Scanning Approach: One-Touch Technique in Second and Third Trimesters
رویکرد اسکن سونوگرافی قبل از تولد: تکنیک یک لمس در سه ماهه دوم و سوم-2021 This study was aimed at evaluating the performance of the innovative technique Smart Fetus (SF) developed to recognize the planes and obtain the basic biometric measurements of fetuses automatically. This prospective study included 1005 uncomplicated singleton pregnancies undergoing routine examinations. For every
pregnancy, planes, including the transverse section of the thalami, transverse section of the abdomen and longitudinal section of the femur, were acquired, and standard biometric measurements, including biparietal diameter,
head circumference, abdominal circumference and femur length, were obtained using SF and traditional ultrasound technique (TUT). The accuracy, reproducibility and time required for the analysis of SF were compared
with those of TUT. In 998 of 1005 cases (99.30%), SF successfully acquired the sections and made all measurements.
The agreement between the techniques was high for all measurements. The time to obtain sections and measure biometric parameters or solely measure biometric parameters was significantly shorter with SF than with TUT. No
significant differences were found in SF repeated measurements obtained by two independent observers. The SF
technique helped in the acquisition of reliable standard sections and biometric measurements and saved time. It
might serve as a novel ultrasound scanning approach and improve workflow efficiency. (E-mail: lishengli63@126.
com) © 2021 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
KeyWords: Artificial intelligence | Biometric measurement | Fetus | Prenatal ultrasonography | Standard plane. |
مقاله انگلیسی |