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1 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |
2 |
A systematic review on computer vision-based parking lot management applied on public datasets
مرور سیستماتیک مدیریت پارکینگ مبتنی بر بینایی ماشین اعمال شده بر روی مجموعه داده های عمومی-2022 Computer vision-based parking lot management methods have been extensively researched upon owing to their
flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot
image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically
crafted to test computer vision-based methods for parking lot management approaches and consequently
present a systematic and comprehensive review of existing works that employ such datasets. The literature
review identified relevant gaps that require further research, such as the requirement of dataset-independent
approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have
noticed that several important factors such as the presence of the same cars across consecutive images, have
been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis
of the datasets also revealed that certain features that should be present when developing new benchmarks,
such as the availability of video sequences and images taken in more diverse conditions, including nighttime
and snow, have not been incorporated.
keywords: Parking lot | Dataset | Benchmark | Machine learning | Image processing |
مقاله انگلیسی |
3 |
An overview of Human Action Recognition in sports based on Computer Vision
مروری بر تشخیص کنش انسانی در ورزش بر اساس بینایی کامپیوتری-2022 Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and
tennis to detect players and recognize their actions and teams activities during training, matches, warm-ups, or
competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the
actions duration, and identify the action type. The main idea of HAR in sports is to monitor a players performance, that is, to detect the player, track their movements, recognize the performed action, compare various
actions, compare different kinds and skills of acting performances, or make automatic statistical analysis.
As an action that can occur in the sports field refers to a set of physical movements performed by a player in
order to complete a task using their body or interacting with objects or other persons, actions can be of different
complexity. Because of that, a novel systematization of actions based on complexity and level of performance and
interactions is proposed.
The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose. keywords: یادگیری ماشین | تشخیص عمل انسانی | سیستم سازی اقدام | مجموعه داده های ورزشی | شناخت کنش انسان در ورزش | ورزش | Machine learning | Human Action Recognition | Action systematization | Sports dataset | Human action recognition in sports | Sport |
مقاله انگلیسی |
4 |
Quantum Kernels for Real-World Predictions Based on Electronic Health Records
هستههای کوانتومی برای پیشبینیهای دنیای واقعی بر اساس پروندههای سلامت الکترونیکی-2022 Research on near-term quantum machine learning has explored how classical machine learning
algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely
classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets,
no work done to date has studied empirically whether the quantum advantage is attainable and with what
data. In this article, we report the first systematic investigation of empirical quantum advantage (EQA) in
healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic
health records data subsets and created a configuration space of 5–20 features and 200–300 training samples.
For each configuration coordinate, we trained classical support vector machine models based on radial basis
function kernels and quantum models with custom kernels using an IBM quantum computer, making this
one of the largest quantum machine learning experiments to date. We empirically identified regimes where
quantum kernels could provide an advantage and introduced a terrain ruggedness index, a metric to help
quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step toward a priori identification of data sets where quantum advantage could
exist.
INDEX TERMS: Artificial intelligence | digital health | electronic health records (EHR) | empirical quantum advantage (EQA) | machine learning | quantum kernels | real-world data | small data sets | support vector machines (SVM). |
مقاله انگلیسی |
5 |
Deep unsupervised methods towards behavior analysis in ubiquitous sensor data
روش های عمیق بدون نظارت برای تجزیه و تحلیل رفتار در داده های حسگر همه جا حاضر-2022 Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of
features for modeling user-specific characteristics. These characteristics, in turn, can be used for a
number of tasks including resource management, power efficiency, and smart home applications.
In recent years, the employment of topic models for BA has been found to successfully extract the
dynamics of the sensed data. Topic modeling is popularly performed on text data for mining
inherent topics. The task of finding the latent topics in textual data is done in an unsupervised
manner. In this work we propose a novel clustering technique for BA which can find hidden
routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently
works on high dimensional data for BA without performing any computationally expensive
reduction operations. We evaluate three different techniques namely Latent Dirichlet Allocation
(LDA), the Non-negative Matrix Factorization (NMF), and the Probabilistic Latent Semantic
Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using
performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets
namely, the Intel Lab, Kyoto, and MERL. Through rigorous experiments, we achieve silhouette
scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset, and 0.8312 over the
MERL dataset for clustering. In these cases, however, it is di cult to validate the results obtained as
the datasets do not contain any ground truth information. Towards that, we investigate a self-
supervised method that will be capable of capturing the inherent ground truths that are avail-
able in the dataset. We design a self-supervised technique which we apply on datasets containing
ground truth and also without. We see that our performance on data without ground truth differs
from that with ground truth by approximately 8% (F-score) hence showing the efficacy of self-
supervised techniques towards capturing ground truth information. keywords: تحلیل داده های فراگیر | تحلیل رفتار | یادگیری خود نظارتی | Ubiquitous data analysis | Behavior analysis | Self supervised learning |
مقاله انگلیسی |
6 |
Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance
طبقه بندی خودکار جانوران در عکس های بستر دریا: تأثیر اندازه مجموعه داده های آموزش و اعتبار سنجی ، با ملاحظاتی برای عدم تعادل کلاس-2021 Machine learning is rapidly developing as a tool for gathering data from imagery and may be useful in identifying (classifying) visible specimens in large numbers of seabed photographs. Application of an automated classifi- cation workflow requires manually identified specimens to be supplied for training and validating the model. These training and validation datasets are generally generated by partitioning the available manual identified specimens; typical ratios of training to validation dataset sizes are 75:25 or 80:20. However, this approach does not facilitate the desired scalability, which would require models to successfully classify specimens in hundreds of thousands to millions of images after training on a relatively small subset of manually identified specimens. A second problem is related to the ‘class imbalance’, where natural community structure means that fewer spec- imens of rare morphotypes are available for model training. We investigated the impact of independent variation of the training and validation dataset sizes on the performance of a convolutional neural network classifier on benthic invertebrates visible in a very large set of seabed photographs captured by an autonomous underwater vehicle at the Porcupine Abyssal Plain Sustained Observatory. We tested the impact of increasing training dataset size on specimen classification in a single validation dataset, and then tested the impact of increasing validation set size, evaluating ecological metrics in addition to computer vision metrics. Computer vision metrics (recall, precision, F1-score) indicated that classification improved with increasing training dataset size. In terms of ecological metrics, the number of morphotypes recorded increased, while diversity decreased with increasing training dataset size. Variation and bias in diversity metrics decreased with increasing training dataset size. Multivariate dispersion in apparent community composition was reduced, and bias from expert-derived data declined with increasing training dataset size. In contrast, classification success and resulting ecological metrics did not differ significantly with varying validation dataset sizes. Thus, the selection of an appropriate training dataset size is key to ensuring robust automated classifications of benthic invertebrates in seabed photographs, in terms of ecological results, and validation may be conducted on a comparatively small dataset with confidence that similar results will be obtained in a larger production dataset. In addition, our results suggest that automated classification of less common morphotypes may be feasible, providing that the overall training dataset size is sufficiently large. Thus, tactics for reducing class imbalance in the training dataset may produce improvements in the resulting ecological metrics. Keywords: Computer vision | Deep learning | Benthic ecology | Image annotation | Marine photography | Artificial intelligence | Convolutional neural networks | Sample size |
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 |
Top management team characteristics and digital innovation: Exploring digital knowledge and TMT interfaces
ویژگی های تیم مدیریت بالا و نوآوری دیجیتال: بررسی دانش دیجیتال و رابط TMT-2021 On their journey toward digital transformation, industrial firms need to embrace digital inno-
vation. The top management team (TMT) is expected to set the course for digital innovation,
which is a challenging endeavour given the novel and cross-functional nature of digital innova-
tion. We draw on role theory to make sense of emerging role requirements for the TMT and
combine this view with upper echelon theory to hypothesize on the specific TMT characteristics
that are needed for digital innovation. We first theorize that firms could benefit from TMT digital
knowledge. Second, we argue that the effective utilization of TMT digital knowledge can be
fostered at internal TMT interfaces, such as between the chief executive officer (CEO), respec-
tively a chief digital officer (CDO), and other top managers. Finally, we consider the TMT hier-
archical structure as a contextual factor in the stimulation of TMT integration processes by
integrative CEOs and CDOs. We employ panel data regressions to a longitudinal dataset of US
industrial firms and find a positive relation between TMT digital knowledge and digital inno-
vation, on average. We additionally find evidence for the integrative roles of CEOs and CDOs.
However, our findings also indicate that the CDO’s integrating role can be hampered by a strong
hierarchical structure in the TMT. keywords: دانش دیجیتال TMT | نوآوری دیجیتال | رابط TMT | افسران ارشد دیجیتال | ساختار سلسله مراتبی TMT | TMT digital knowledge | Digital innovation | TMT interfaces | Chief digital officers | TMT hierarchical structure |
مقاله انگلیسی |
9 |
DEGAN : شبکه های مولد متخاصم غیر متمرکز
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 23 در این مطالعه، یک چارچوب توزیع شده و غیرمتمرکز از شبکه های مولد متخاصم (GAN) بدون تبادل داده های آموزشی پیشنهاد شد. هر گره شامل مجموعه ی از داده محلی ، یک تفکیک کننده کننده و یک مولد است که فقط گرادیان ژنراتور آن با سایر گره ها به اشتراک گذاشته می شوند. در این مقاله ، تکنیک توزیع جدید معرفی می شود که در آن کارکنان مستقیماً با یکدیگر ارتباط برقرار می کنند و هیچ گره مرکزی وجود ندارد. نتایج تجربی ما در مجموعه داده های معیار ، عملکرد و دقت تقریباً یکسانی را در مقایسه با چارچوب های GAN متمرکز موجود نشان می دهد. چارچوب پیشنهادی به عدم یادگیری غیرمتمرکز برای GAN ها می پردازد.
کلمات کلیدی: یادگیری عمیق | شبکه های مولد متخاصم | یادگیری ماشین توزیع شده | معماری غیرمتمرکز |
مقاله ترجمه شده |
10 |
Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks
حسابداری برای تخریب موانع ایمنی در ارزیابی ریسک سیستم های نفت و گاز توسط شبکه های چندگانه بیزی-2021 In this paper, a multistate Bayesian Network (BN) is proposed to model and evaluate the functional performance
of safety barriers in Oil and Gas plants. The nodes of the BN represent the safety barriers Health States (HSs) and
the corresponding conditional Failure Probability (FP) values are assigned. HSs are assessed on the basis of
specific Key Performance Indicators (KPIs) related to the barrier characteristics (i.e., technical, procedural or
organizational, continuously monitored or event-based characterized). FP values are estimated from failure
datasets (for technical barriers), evaluated by Human Reliability Analysis (HRA) (for operational and organi-
zational barriers) and assigned by expert elicitation (for barriers lacking data or information). For illustration,
the multistate BN model is developed for preventive barriers and applied to a case study related to the potential
release of flammable material in the slug catcher of a representative O&G Upstream plant which may lead to
major accident scenarios (fire, explosion, toxic dispersion). The results from the case study demonstrate that the
multistate BN model is able to account for the safety barriers HS and their associated functional performance. keywords: ارزیابی ریسک کمی | ارزیابی خطر زندگی | شبکه بیزی | مانع ایمنی | شاخص عملکرد کلیدی | حاشیه ایمنی احتمالی | Quantitative Risk Assessment | Living Risk Assessment | Bayesian Network | Safety Barrier | Key Performance Indicator | Probabilistic Safety Margins |
مقاله انگلیسی |