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1 |
Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022 The increasing global population and the growing demand for high-quality products have called
for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre-
dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in
Ontario, Canada, asking them to describe their experience of working with IoT and related
technologies. One main discourse with two opposing tendencies was identified: farmers recognize
their relationship with IoT and related technology and view technology as a kind of “employee”,
but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful
Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech-
nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature
on organizational behavior and identify potential outcomes of these beliefs. The results suggest
that a farmer’s style of approaching technology can be assessed on a similar scale as managers’
view of their employees and provide a framework for further research. keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology |
مقاله انگلیسی |
2 |
AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
3 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
4 |
Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022 This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap-
proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are
employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the
properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym-
metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern
(ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is
enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification
directly without the need for texture descriptors. Experimental results with a dataset containing real-world
concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the
Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a
classification accuracy rate of roughly 99%. keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning |
مقاله انگلیسی |
5 |
تجزیه و تحلیل پوششی داده مبتنی بر نسبت: یک رویکرد تعاملی برای شناسایی معیار
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 40 در دنیای واقعی ما با موارد زیادی مواجه هستیم که در آن نسبت داده های ورودی/خروجی برای مدیران بسیار مهم است، بنابراین در این رابطه نمی توان از مدل های سنتی تحلیل پوششی داده (DEA) برای ارزیابی کارایی واحدهای تصمیم گیری (DMU) استفاده کرد، و باید از مدل های DEA بر اساس داده های نسبت بهره برد. برای بدست آوردن معیار مربوطه برای هر واحد تصمیمگیری ناکارآمد، باید ورودیها و خروجیها را به ترتیب کاهش و افزایش دهیم و به یک پیشبینی واحد و منسجم تصمیمگیرنده در مرز کارایی برسیم. در این مقاله ما یک مدل برنامهریزی خطی چندهدفه (MOLP) (multi-objective linear programming) را برای ارزیابی کارایی بر اساس تعریف مجموعه امکان تولید در حضور دادههای نسبت و به دست آوردن معیار مربوطه برای هر واحد تصمیمگیری DMU ارائه میکنیم. ما از روش تعاملی زایونتس و والنیوس (Z-W) برای حل مدل MOLP ارائه شده استفاده میکنیم. با استفاده از تنظیم هدف توسط مدیر از بین راه حل های حاصل از مسئله MOLP، بهترین راه حل را با توجه به ترجیحات مدیران به عنوان معیار انتخاب می کنیم و در پایان نتایج تحقیق را ارائه می کنیم.
واژگان کلیدی: کارایی | DEA-R | معیار | برنامه ریزی چند هدفه | روش تعاملی |
مقاله ترجمه شده |
6 |
A Survey of Indoor Location Technologies, Techniques and Applications in Industry
بررسی فن آوری ها، تکنیک ها و کاربردهای مکان داخلی در صنعت-2022 The recent academic research surrounding indoor positioning systems (IPS) and indoor location-
based services (ILBS) are reviewed to establish the current state-of-the-art for IPS and ILBS. This
review is focused on the use of IPS / ILBS for cyber-physical systems to support secure and safe
asset management (including people as assets), exploring the potential applications of IPS for
industry as suggested in the literature. Current application areas in industry are presented,
separated into physical item and human traceability applications for context. The literature are
reviewed to identify gaps in the ILBS development for industrial applications, future research
needs to focus a development framework to enable scalable solutions for industry. The key gaps
identified in the literature are: (i) a lack of pathways to extend IPS research into an ILBS, (ii) no
end-to-end ILBS have been developed and (iii) no framework has been reported that outlines the
information pathways from sensor data collection and location information to an established
ILBS. The technologies reviewed are presented in a comparison table (Table 1) intended as a
reference for selecting technologies for future systems based on requirements. The techniques
used to extract location information from each of the technologies identified are also explored
stating current accuracy and aligning the techniques with their suitable technologies.
1. Introduction keywords: موقعیت داخلی | اینترنت اشیا | سیستم های حسگر | خدمات مبتنی بر مکان داخلی | سیستم های فیزیکی سایبری | Indoor location | Internet of things | Sensor systems | Indoor location based services | Cyber physical systems |
مقاله انگلیسی |
7 |
A survey on security in internet of things with a focus on the impact of emerging technologies
بررسی امنیت در اینترنت اشیا با تمرکز بر تاثیر فناوری های نوظهور-2022 Internet of Things (IoT) have opened the door to a world of unlimited possibilities for imple-
mentations in varied sectors in society, but it also has many challenges. One of those challenges is
security and privacy. IoT devices are more susceptible to security threats and attacks. Due to
constraints of the IoT devices such as area, power, memory, etc., there is a lack of security so-
lutions that are compatible with IoT devices and applications, which is leading this world of
securely connected things to the “internet of insecure things.” A promising solution to this
problem is going beyond the standard or classical techniques to implementing the security so-
lutions in the hardware of the IoT device. The integration of emerging technologies in IoT net-
works, such as machine learning, blockchain, fog/edge/cloud computing, and quantum
computing have added more vulnerable points in the network. This paper introduces a
comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how
emerging technologies such as machine learning and blockchain are integrated in IoT, challenges
resulted from this integration, and potential solutions to these challenges. The paper utilizes the
4-layer IoT architecture as a reference to identify security issues with corresponding solutions. keywords: اینترنت اشیا | امنیت | فراگیری ماشین | بلاک چین | تهدیدها | راه حل های امنیتی | IoT | Security | Machine learning | Blockchain | Threats | Security solutions |
مقاله انگلیسی |
8 |
A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city
بررسی سیستماتیک فناوری ها و راه حل ها برای بهبود امنیت و حفاظت از حریم خصوصی شهروندان در شهر هوشمند-2022 The development of smart cities through digital communications has improved citizens’ quality of
life and well-being. In these cities, IoT technology generates vast amounts of data at any given
time, which is analyzed to provide services to citizens. In the proper implementation of these
cities, a critical challenge is the violation of citizens’ privacy and security, which leads to a lack of
trust and pessimism toward the services of the smart city. To ensure citizens’ participation, smart
city developers should adequately protect their security and privacy from gaining their trust. If
citizens don’t want to participate, the main benefits of a smart city will be lost. This article
presents a comprehensive review of smart city security issues and privacy. It provides a basis for
categorizing current and future developments in this area and developing a thematic classifica-
tion to highlight the requirements and security strategies for designing a smart and safe city. The
paper identifies current security and privacy solutions and describes open research challenges and
issues. An output of this study is a systematic map of literature on the subject that identifies
critical concepts, evidence, challenges, solutions, and gaps. It summarizes the findings into a body
of evidence that has previously been heterogeneous and complex. keywords: مرور سیستماتیک توصیفی | شهر هوشمند | اینترنت اشیا | حریم خصوصی | امنیت | Descriptive systematic review | Smart city | IoT | Privacy | Security |
مقاله انگلیسی |
9 |
Cyber Physical Systems: Analyses, challenges and possible solutions
سیستم های فیزیکی سایبری: تحلیل ها، چالش ها و راه حل های ممکن-2022 It is becoming more difficult to protect the authentication of our data in todays world of smart living. On the one
hand, we are able to live in smart homes and smart cities with ease. Even if we use the most complicated
passwords, we cant be sure that the Internet of Things and the Internet of Everything are safe. One way to make
sure people and things are safe is to use Multi-Factor Authentication. Also, a big and complicated system needs
more efficient and robust solutions for real, and strong, security, so this is important. There are a lot of smart ways
to solve problems today. For this reason, the internet of things is being used in every possible field or application.
This new ecosystem, which is called Cyber Physical Systems, was built by IoTs. Cyber-Physical Systems use
computing, communication, and control to make new technology or the next generation of engineered systems. In
the last decade, there has been a lot of work done on cyber physical systems that we didnt expect. There have
been a lot of threats, challenges, and important issues in the last decade. We have a big problem with the security
of CPS because the basic blocks used to make them are very different. Even if were talking about natural gas
systems or transportation systems or other automated systems, they all have something to do with CPS, no matter
what. These days, CPSs systems are used for energy, transportation, the environment, and health care, among
other things. This article talks about a number of problems that need to be solved by researchers and scientists
(working related to respective area, i.e., CPS). As a result, this article also talks about a partial survey of important
research issues, and an overview of several research projects that have been done in the last decade by a number
of different people to improve CPS.
keywords: سیستم های فیزیکی-سایبری | اینترنت اشیا | اینترنت همه چیز | چالش ها | امنیت و حریم خصوصی | Cyber-physical systems | Internet of things | Internet of everything | Challenges | Security, and privacy |
مقاله انگلیسی |
10 |
Nurses perspectives on pain management practices during newborn blood sampling in China
دیدگاه های پرستاران در مورد شیوه های مدیریت درد در طی نمونه گیری خون نوزادان در چین-2021 Introduction: Nurses’ use of evidence-based pain treatments for newborns during needle-related procedures in
China was unknown. This study aimed to ascertain knowledge and use of pain management strategies and
usefulness of a publicly accessible ’BSweet2Babies’ video, produced in Mandarin, demonstrating the use of
breastfeeding, skin-to-skin care (SSC), and sweet solutions during painful procedures.
Methods: An online survey was conducted during six nursing conferences in China ascertaining nurses’ previous
viewing of the video and knowledge and use of the demonstrated strategies.
Results: 221 nurses participated. Only 25 (11.3%) had previously seen the video. Over half knew that breast-
feeding (n = 138, 62.4%) and SSC (n = 173, 78.3%) reduced pain, and 89 (40.3%) knew that sucrose reduced
pain, but these strategies were infrequently used. Most intended to use the strategies in the future.
Discussion: A knowledge-to-action gap for newborn pain management was identified. Future research is needed to
improve the implementation of effective pain treatment for newborns. keywords: نوزاد | درد رویه ای | مدیریت درد | تمرین مبتنی بر شواهد | نظر سنجی | Neonate | Procedural pain | Pain management | Evidence-based practice | Survey |
مقاله انگلیسی |