با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیشبینی تعامل رسانههای اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022 In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta-
grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’
Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to
social media engagement. Results demonstrate that food images that are more confidently evaluated by Google
Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-
up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier
to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and
food industry trends, the more typical a food appears, the more social media engagement it receives. Using
Google Vision AI to identify what product offerings receive engagement presents an accessible method for
marketers to understand their industry and inform their social media marketing strategies. keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI |
مقاله انگلیسی |
2 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
3 |
رونق در یک چشم انداز در حال تغییر: نقش رسانه های اجتماعی در حمایت از استراتژی کسب و کار
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 19 سازمان های معاصر برای شناخت بهتر مشتریان خود نیاز به برقراری ارتباط با مشتریان خود دارند تا آنها بتوانند یاد بگیرند که چگونه نیازهای خود را بهتر ارضا کنند، خدمات بهتری به مشتریان ارائه دهند و از این طریق عملکرد تجاری خود را بهبود بخشند. رسانههای اجتماعی یکی از انواع کانالهای ارتباطی هستند که سازمانها در حال حاضر از آن برای حمایت از این استراتژیها استفاده میکنند. هدف این مقاله بررسی این موضوع است که چگونه انواع مختلف سازمانها از رسانههای اجتماعی برای حمایت از استراتژیهای تجاری خود استفاده میکنند و چگونه این امر بر سازمانها تأثیر میگذارد.کارایی. برای دستیابی به این هدف، این پژوهش اطلاعاتی را از چندین سازمان در تهران، پایتخت ایران و 58 نفر از مدیران، کارشناسان بازاریابی، کارشناسان رسانههای اجتماعی و اساتید دانشگاه جمعآوری میکند. در این پژوهش برای جمع آوری داده ها از پرسشنامه استفاده شده و برای تجزیه و تحلیل داده ها از روش های آماری توصیفی- استنباطی استفاده شده است. نتایج نشان میدهد که رسانههای اجتماعی نقش مهم و در عین حال متفاوتی در حمایت از استراتژیهای توسعه کسبوکار جستجوگر، تحلیلگر، راکتور و مدافع دارند که عناصر کلیدی در بهبود عملکرد کسبوکار هستند.
کلید واژه ها: استراتژی کسب و کار | رسانه های اجتماعی | عملکرد تجاری |
مقاله ترجمه شده |
4 |
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 |
5 |
Adverse Reaction Detection from Social Media based on Quantum Bi-LSTM with Attention
تشخیص واکنش نامطلوب از رسانه های اجتماعی بر اساس کوانتوم Bi-LSTM با توجه-2022 Drug combination is very common in the course of disease treatment. However, it inevitably
increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately
detect and identify the potential ADRs for combined medication safety and public health. Social media is an
important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter,
highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in
terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power.
Quantum computing has strong parallel computing capability, and requires less computing power. By
introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory
(Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection
from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked.
Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in
input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network
structure, and the weight and active value qubits of the model are used to update the network weight. The
performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional
ADR detection method and three deep learning based ADR detection approaches. The experiment results
show that the proposed method has great potential in ADRs detection. To the best of our knowledge, this is
the first time to investigate quantum computing to detect ADRs from social media big data.
INDEX TERMS: Social media big data | Adverse drug reactions (ADRs) | Bi-directional Long Short-Term Memory (Bi-LSTM) | Quantum Bi-LSTM with attention (QBi-LSTMA). |
مقاله انگلیسی |
6 |
Algebraic Attacks on Block Ciphers Using Quantum Annealing
حملات جبری به رمزهای بلوکی با استفاده از آنیل کوانتومی-2022 Drug combination is very common in the course of disease treatment. However, it inevitably
increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately
detect and identify the potential ADRs for combined medication safety and public health. Social media is an
important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter,
highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in
terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power.
Quantum computing has strong parallel computing capability, and requires less computing power. By
introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory
(Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection
from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked.
Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in
input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network
structure, and the weight and active value qubits of the model are used to update the network weight. The
performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional
ADR detection method and three deep learning based ADR detection approaches. The experiment results
show that the proposed method has great potential in ADRs detection. To the best of our knowledge, this is
the first time to investigate quantum computing to detect ADRs from social media big data.
INDEX TERMS: Social media big data | Adverse drug reactions (ADRs) | Bi-directional Long Short-Term Memory (Bi-LSTM) | Quantum Bi-LSTM with attention (QBi-LSTMA). |
مقاله انگلیسی |
7 |
A Methodology For Large-Scale Identification of Related Accounts in Underground Forums
یک روش برای شناسایی در مقیاس بزرگ حساب های مرتبط در انجمن های زیرزمینی-2021 Underground forums allow users to interact with communities focused on illicit activities.
They serve as an entry point for actors interested in deviant and criminal topics. Due to the
pseudo-anonymity provided, they have become improvised marketplaces for trading illegal
products and services, including those used to conduct cyberattacks. Thus, these forums
are an important data source for threat intelligence analysts and law enforcement. The use
of multiple accounts is forbidden in most forums since these are mostly used for malicious
purposes. Still, this is a common practice. Being able to identify an actor or gang behind
multiple accounts allows for proper attribution in online investigations, and also to design
intervention mechanisms for illegal activities. Existing solutions for multi-account detec-
tion either require ground truth data to conduct supervised classification or use manual
approaches. In this work, we propose a methodology for the large-scale identification of re-
lated accounts in underground forums. These accounts are similar according to the distinc-
tive content posted, and thus are likely to belong to the same actor or group. The methodol-
ogy applies to various domains and leverages distinctive artefacts and personal information
left online by the users. We provide experimental results on a large dataset comprising more
than 1.1M user accounts from 15 different forums. We show how this methodology, com-
bined with existing approaches commonly used in social media forensics, can assist with
and improve online investigations.
© 2021 Elsevier Ltd. All rights reserved. keywords: رسانه های اجتماعی قانونی | انجمن های زیرزمینی | اندازه گیری در مقیاس بزرگ | حساب های مرتبط | سایبری | Social media forensics | Underground forums | Large-Scale measurement | Related accounts | Cybercrime |
مقاله انگلیسی |
8 |
I-SOCIAL-DB: A labeled database of images collected from websites and social media for Iris recognition
I-SOCIAL-DB: پایگاه داده برچسب گذاری شده از تصاویر جمع آوری شده از وب سایت ها و رسانه های اجتماعی برای تشخیص عنبیه-2021 People upload daily a huge number of portrait face pictures on websites and social media, which can be processed using biometric systems based on the face characteristics to perform an automatic recognition of the individuals. However, the performance of face recognition approaches can be limited by negative factors as aging, occlusions, rotations, and uncontrolled expressions. Nevertheless, the constantly increasing quality and resolution of the portrait pictures uploaded on websites and social media could permit to overcome these problems and improve the robustness of biometric recognition methods by enabling the analysis of additional traits, like the iris. To point the attention of the research community to the possible use of iris-based recognition techniques for images uploaded on websites and social media, we present a public image dataset called I-SOCIAL-DB (Iris Social Data- base). This dataset is composed of 3,286 ocular regions, extracted from 1,643 high-resolution face images of 400 individuals, collected from public websites. For each ocular region, a human expert extracted the coordinates of the circles approximating the inner and outer iris boundaries and performed a pixelwise segmentation of the iris contours, occlusions, and reflections. This dataset is the first collection of ocular images from public websites and social media, and one of the biggest collections of manually segmented ocular images in the literature. In this paper, we also present a qualitative analysis of the samples, a set of testing protocols and figures of merit, and benchmark results achieved using publicly available iris segmentation and recognition algorithms. We hope that this initiative can give a new test tool to the biometric research community, aiming to stimulate new studies in this challenging research field.© 2020 Elsevier B.V. All rights reserved. Keywords: Biometrics | Iris | Web images |
مقاله انگلیسی |
9 |
Evaluating Chinese government WeChat official accounts in public service delivery: A user-centered approach
ارزیابی حساب های رسمی دولت چین WeChat در ارائه خدمات عمومی: یک رویکرد کاربر محور-2021 WeChat official accounts have been increasingly adopted by Chinese government agencies to deliver public
services, in response to the “Internet + Public Service” reformation. While previous studies depended heavily on
the expert-oriented approach to evaluate the accounts, this paper presents a user-centered study based on a
mixed methods research design in which an unobtrusive clickstream data analysis was complemented by a card
sorting study, stakeholder interviews, and a focus group. A 2-month server log file containing 42,188,760
clickstream records was obtained from an active government WeChat official account and analyzed at the
movement level, which found that the account was mainly used as a lookup tool with most services underutilized
and its home portals failed to support effective wayfinding to needed services. Deficiencies in information ar-
chitecture, operation strategy, and interaction design of the account were identified in the complementary
studies. This study not only enriches the knowledge about social media use in the Chinese government for public
service delivery, but also introduces innovative methods to generate new research insights. The findings can
inform government WeChat official accounts of how to improve service quality and user experience. keywords: حساب های رسمی دولت WeChat | تحویل خدمات عمومی | تجزیه و تحلیل داده های Clickstream | روش های مختلف طراحی | رویکرد کاربر محور | Government WeChat official accounts | Public service delivery | Clickstream data analysis | Mixed methods design | User-centered approach |
مقاله انگلیسی |
10 |
Special interest tourism is not so special after all: Big data evidence from the 2017 Great American Solar Eclipse
جهانگردی با علاقه ویژه از همه مهم تر نیست: شواهد داده های بزرگ از خورشید گرفتگی بزرگ آمریکایی 2017-2020 This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism
(SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We
utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data
analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns.
Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is
validated with the external data through hypothesis testing and compared with and contrasted against existing
motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is
traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking
novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban
destinations for developing SIT tourism are also discussed. Keywords: Big data | Instagram photos | Social media | Spatial analysis | Special interest tourism | Astro-tourism |
مقاله انگلیسی |