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1 |
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 |
2 |
Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمانبندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینهسازی ترکیبی توسط محاسبات کوانتومی-2022 Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in
6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with
one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit
time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate
each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting
in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem,
we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing.
This will lead to the optimization of the entire system at a high speed and low power consumption in the future.
Using computer simulation, we clarified that the proposed method realizes a more efficient communication
compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization |
مقاله انگلیسی |
3 |
Quantum Computing Based Optimization for Intelligent Reflecting Surface (IRS)-Aided Cell-Free Network
بهینهسازی مبتنی بر محاسبات کوانتومی برای شبکههای بدون سلول با کمک سطح بازتابی هوشمند (IRS)-2022 Intelligent reflecting surface (IRS) enables the control of propagation characteristics and is attracting considerable attention
as a technology to improve energy utilization efficiency in 6th generation mobile communication systems. As cell-free networks with
multiple distributed base stations (BSs) can communicate in a coordinated manner, they are being actively researched as a new
network architecture to resolve the problem of inter-cell interference in conventional cellular networks. The introduction of the IRS into
the cell-free network can avoid shadowing at a lower cost with less power consumption. Thus, in this study, we considered the case of
communication with user equipment (UE) in a shadowing environment using IRS in a cell-free network that contained distributed BSs
with a single antenna. Moreover, the selection of multiple access methods was derived according to the numbers of BSs, IRSs, and
UEs. In addition, we proposed a quadratic unconstrained binary optimization formulation to optimize the IRS reflection coefficient using
quantum computing. The simulation results verified that the application of the proposed method resulted in a more efficient
communication. Thus, this study clarifies that the optimum control method in every communication environment and aims to act as a
stepping stone to optimize the entire cell-free system.
Index Terms: Intelligent Reflecting Surface | Cell-Free Network | Quantum Computing | Quantum Annealing | Combinatorial Optimization. |
مقاله انگلیسی |
4 |
Quantum Distributed Unit Commitment: An Application in Microgrids
تعهد واحد توزیع شده کوانتومی: یک کاربرد در ریزشبکه ها-2022 The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment
problem complexity is expected to increase in the future because
of the trend toward the increase of penetration of intermittent
renewables. Even though quantum computing has proven effective
for solving a host of problems, its applications for power systems’
problems have been rather limited. In this paper, a quantum unit
commitment is innovatively formulated and the quantum version
of the decomposition and coordination alternate direction method
of multipliers (ADMM) is established. The above is achieved by
devising quantum algorithms and by exploiting the superposition
and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible
solutions. The main contributions of this paper include: 1) the
innovative development of a quantum model for Unit Commitment;
2) development of decomposition and coordination-supported
framework which paves the way for the utilization of limited
quantum resources to potentially solve the large-scale discrete
optimization problems; 3) devising the novel quantum distributed
unit commitment (QDUC) to solve the problem in a larger scale
than currently available quantum computers are capable of solving.
The QDUC results are compared with those from its classical
counterpart, which validate the efficacy of quantum computing.
Index Terms: Microgrids | quantum computing | quantum distributed optimization | unit commitment. |
مقاله انگلیسی |
5 |
الگوریتم ژنتیک چند هدفه و طرح معماری یادگیری عمیق مبتنی بر CNN برای تشخیص موثر spam
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18 معمولا ایمیل به عنوان قدرتمندترین رسانه در شبکههای اجتماعی آنلاین در نظر گرفته میشود که امکان گفتگو و ارتباط آنلاین کاربران رسانههای اجتماعی آنلاین را با یکدیگر فراهم می کند، همچنین امکان اشتراک گذاری لینک هم وجود دارد. به ویژه، توییتر به عنوان محبوب ترین شبکه اجتماعی شناخته شده است که بهترین کانال ارتباطی برای به اشتراک گذاشتن اخبار، ایده ها، افکار، نظرات و عقاید فعلی کاربران خود با سایر کاربران رسانه های اجتماعی آنلاین است. علیرغم تلاشهایی که برای مبارزه با عملیات اسپم در شبکه اجتماعی آنلاین انجام شده است، اسپم توییتر دارای عملکرد جدیدی محدود به 140 کاراکتر است. این نه تنها علت اصلی آزار کاربران روزمره است، بلکه اکثر مسائل امنیتی رایانه نیز ناشی از آن است که میلیاردها دلار کاهش بهره وری هزینه را در پی دارد. در این مقاله، یک الگوریتم ژنتیک چندهدفه و یک طرح معماری یادگیری عمیق مبتنی بر CNN (MOGA-CNN-DLAS) برای فرآیند تشخیص اسپم غالب در توییتر پیشنهاد میکنیم. جزئیات تجربی و نتایج و بحث حاصل از MOGA-CNN-DLAS پیشنهادی از نظر دقت ، صحت، فراخوان، FScore، RMSE و MAE مورد ارزیابی قرار گرفتند. این ارزیابی با تغییر نسبت دادههای آموزشی کاربردی از سه مجموعه داده واقعی، مانند مجموعه داده توییتر k100 و ASU انجام شد.
کلمات کلیدی: اسپم توییتر | یادگیری عمیق | شبکه عصبی پیچشی یا همگشتی (CNN) | الگوریتم ژنتیک | آنالیز رسانه های اجتماعی | تشخیص موثر اسپم |
مقاله ترجمه شده |
6 |
Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers
حملات بهره برداری شاتل و دفاع آنها در کامپیوترهای کوانتومی یونی به دام افتاده-2022 Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing.
TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in
superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the
horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple
users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers
as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the
short wait queue. However, shared access to quantum computers can create new security issues. This paper
presents one such vulnerability in shared TI systems that require shuttle operations for communication among
traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations
(fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting
attacks can substantially degrade the fidelities of victim programs by ≈2× to ≈63×. Finally, we present
several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with
dummy qubits, and capping maximum shuttles.
INDEX TERMS: Trapped-ion | qubit | quantum computing | shuttle | security | fidelity. |
مقاله انگلیسی |
7 |
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022 Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having
applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for
optimally routing transportation of goods by vehicles at a given
set of locations. This paper discusses the broader problem of
vehicle traffic management, more popularly known as the Vehicle
Routing Problem (VRP), and investigates the possible use of
near-term quantum devices for solving it. For this purpose,
we give the Ising formulation for VRP and some of its constrained
variants. Then, we present a detailed procedure to solve VRP
by minimizing its corresponding Ising Hamiltonian using a
hybrid quantum-classical heuristic called Quantum Approximate
Optimization Algorithm (QAOA), implemented on the IBM
Qiskit platform. We compare the performance of QAOA with
classical solvers such as CPLEX on problem instances of up to
15 qubits. We find that performance of QAOA has a multifaceted
dependence on the classical optimization routine used, the depth
of the ansatz parameterized by p, initialization of variational
parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms. |
مقاله انگلیسی |
8 |
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 |
9 |
A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks
مدل تشخیص نفوذ مه-ابر دو لایه برای شبکه های اینترنت اشیا-2022 The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However,
the open deployment environment and the limited resources of IoT devices make them vulnerable
to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks
that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks
while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes
the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog
layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus
enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed
extensive experiments using two publicly available datasets to test the proposed approach. Test
results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also
justified the proposed model in terms of improved service time, lower delay, and optimal energy
utilization.
keywords: Fog computing | Intrusion detection | IoT network | Machine learning | Security |
مقاله انگلیسی |
10 |
An efficient radio-frequency spectrum utilization technique for cognitive radio networks
یک تکنیک کارآمد استفاده از طیف فرکانس رادیویی برای شبکههای رادیویی شناختی-2022 Cognitive radio (CR) is a rising technology that unlocks the doors for radio spectrum scarcity
problem, which is of great concern nowadays among the researchers at various levels. CR
enables to unlicensed users or secondary users (SUs) to access the primary channels when
licenced users or primary users (PUs) are not using these channels. Two fundamental access
methods namely overlay and underlay are very popular in utilizing the free available channels
or the white spaces. Various hybrid access methods have been proposed and recommended by
many researchers to further enhance the radio spectrum utilization. Of course hybrid access
methods are the better ways to deal this spectrum scarcity problem, but a comprehensive
and directed effort is required to optimize the modality of these methods. This work proposes
a Markov chain based hybrid access method named as Hybrid Spectrum Utilization Technique
(HSUT), which tries to maximize the radio spectrum utilization by enhancing the PU’s detection
probability. This work also analyzes the performance of the HSUT and results obtained through
OMNeT++ simulator are very encouraging. At last, this work also compares the performance of
the HSUT with the overlay, underlay, Hybrid-P1 (Dhurandher et al., 2021), and the Hybrid-P2
(Dhurandher et al., 2021) access methods.
keywords: Cognitive radio | White spaces | Hybrid access | Markov chain | OMNeT++ |
مقاله انگلیسی |