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تعداد مقالات یافته شده: 78
ردیف عنوان نوع
1 A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022
Deep learning has been widely applied in various fields such as computer vision, natural language pro- cessing, and data mining. Although deep learning has achieved significant success in solving complex problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result- ing in models that fail to perform their tasks properly, which limits the application of deep learning in security-critical areas. In this paper, we first review some of the classical and latest representative adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl- edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze the subject development in this field based on the information of 5923 articles from Scopus. In the end, possible research directions for the development about adversarial attacks are proposed based on the trends deduced by keywords detection analysis. All the data used for visualization are available at: https://github.com/NanyunLengmu/Adversarial- Attack- Visualization .
keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis
مقاله انگلیسی
2 Equivalence Checking of Quantum Circuits With the ZX-Calculus
بررسی هم ارزی مدارهای کوانتومی با ZX-calculus-2022
As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for automated methods to design and test potential applications rises. Equivalence checking of quantum circuits is an important, yet hardly automated, task in the development of the quantum software stack. Recently, new methods have been proposed that tackle this problem from widely different perspectives. One of them is based on the ZX-calculus, a graphical rewriting system for quantum computing. However, the power and capability of this equivalence checking method has barely been explored. The aim of this work is to evaluate the ZX-calculus as a tool for equivalence checking of quantum circuits. To this end, it is demonstrated how the ZX-calculus based approach for equivalence checking can be expanded in order to verify the results of compilation flows and optimizations on quantum circuits. It is also shown that the ZX-calculus based method is not complete—especially for quantum circuits with ancillary qubits. In order to properly evaluate the proposed method, we conduct a detailed case study by comparing it to two other state-of-the-art methods for equivalence checking: one based on path-sums and another based on decision diagrams. The proposed methods have been integrated into the publicly available QCEC tool (https://github.com/cda-tum/qcec) which is part of the Munich Quantum Toolkit (MQT).
Index Terms: Quantum computing | formal verification | quantum circuit.
مقاله انگلیسی
3 Alternative bayesian models for genetic evaluation of biometrical, physical, and morphological reproductive traits in nelore bulls
مدلهای جایگزین بایزی برای ارزیابی ژنتیکی صفات تولید مثل بیومتریک ، فیزیکی و مورفولوژیکی در گاو نرهای-2021
Reproductive performance is one of the most important factors for the productive efficiency in beef cattle pro- duction. Biometric testicular and physical and morphological traits of the ejaculate are used to evaluate the reproductive performance of bulls. The phenotypic data of semen physical and morphological traits are expressed in percentage or notes, thus the evaluation of such traits through models that assume normal data distribution can be questioned. We aimed to compare the mixed models fitted under alternative and traditional Gaussian distributions for physical and morphological semen traits. Additionally to identify the most suitable model, we aimed also to predict genetic parameters for reproductive traits via Bayesian inference. Phenotypic data of 615 Nelore bulls, aged between 18 and 36 months, were used. The traits sperm motility (MOT), major (MD), minor (MID), and total (TD) sperm defects and percentage of normal spermatozoa were evaluated through Gaussian and Exponential models. For the physical traits expressed in scores, sperm vigor (VIG), and semen mass activity (MASS), the Gaussian and Poisson models were compared. Only Gaussian model was used for genetic parameters estimation of biometric testicular and seminal vesicle traits. The exponential model presented a better fit quality for MD and MID data than Gaussian model. For MASS the best model was Poisson. For all other evaluated traits, the Gaussian model presented the best fit. Heritability estimates were high for testicular biometric traits, ranging from 0.34 to 0.5. However, for the biometric measures of the seminal vesicle the heritabilitys were low (0.04 for seminal vesicle length and 0.07 for seminal vesicle width). For the morphological traits of the semen, the heritability estimates were high, ranging from 0.36 to 0.50. For the semen physical traits, the heritability estimates varied widely, from 0.04 for MOT and VIG to 0.57 for MASS. The model assumption influences the bull genetic evaluation for physical and morphological semen traits, resulting in substantial ranking differences. However, the Gaussian model exhibited the best prediction accuracies for all traits.* Corresponding author.E-mail address: tulio.boas@ufv.br (T.V.V.B. Oliveira).https://doi.org/10.1016/j.livsci.2020.104313Received 1 July 2020; Received in revised form 1 October 2020; Accepted 27 October 2020Available online 28 October 20201871-1413/© 2020 Elsevier B.V. All rights reserved.
Keywords: Bovine | Bayesian inference | Reproductive traits | Bull fertility
مقاله انگلیسی
4 Sexual-predator Detection System based on Social Behavior Biometric (SSB) Features
سیستم تشخیص جنسی-شکارچی بر اساس ویژگی های بیومتریک رفتار اجتماعی (SSB)-2021
This study designs an online sexual predator detection system using Social Behavior Biometric (SSB) features. Social biometric focuses on extracting the pattern a user exhibits while interacting and communicating through social networks. The paper addresses the online sexual predator problem by mining the vocabulary and emotional behavior, which could assist in identifying if the user is a benign or predator. The feature-set consists of vocabulary terms that appear differently in predator and victim content. In order to strengthen the detection model, the paper also focuses on distinguishing the two classes of users based on emotions reflected in their conversation. The experiments are performed on the PAN 2012 corpus. Two datasets are created with respect to vocabulary-based and emotion-based features. The results obtained on the test set have proved that by integrating the vocabulary and emotion-based attributes, the performance of the system is significantly enhanced. While comparing, the proposed approach has outperformed top existing methods by obtaining F1, F2, and F0.5values of 0.95, 0.94, and 0.96 respectively. Furthermore, we also recorded the best accuracy compared to state-of-the-art studies for our proposed SBB-based approach with 99.86%, 99.51%, and 99.88% for Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) respectively.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 5th International Conference on AI in Computational Linguistics.
Keywords: Online Sexual Predators | Emotion mining | Lexical analysis Machine Learning
مقاله انگلیسی
5 EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications
EBAPy: یک چارچوب پایتون برای تجزیه و تحلیل عوامل موثر بر عملکرد برنامه های مبتنی بر EEG-2021
EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human–computer interaction in the near future.Code metadataCurrent code version v1.1Permanent link to code/repository used for this code version https://github.com/SoftwareImpacts/SIMPAC-2021-2Permanent link to Reproducible Capsule https://codeocean.com/capsule/4497139/tree/v1Legal Code License MITCode versioning system used gitSoftware code languages, tools, and services used Python Compilation requirements, operating environments & dependencies If available Link to developer documentation/manualSupport email for questions dustin.carrion@gmail.com
Keywords: EEG-based applications | Recording time | Discrete wavelet transform
مقاله انگلیسی
6 Technical-knowledge-integrated material flow cost accounting model for energy reduction in industrial wastewater treatment
مدل حسابداری مواد مخدر فنی دانش فنی برای کاهش انرژی در درمان فاضلاب صنعتی-2021
A novel simulation model incorporating the concept of material flow cost accounting (MFCA) into a numerical process simulator for wastewater treatment plants (WWTPs) was developed. Cost-related parameters, such as electrical power consumption, were calculated for each unit process by referring to predetermined formulas of design rules and technical knowledge built into the model. These calculated values were then assigned to the outflow stream proportional to the flowrate, allowing each flow stream in the WWTP to be quantified according to the history of assigned costs. This method increased the number of quantity centers in MFCA models regardless of actual data availability, thus contributing complex flow configuration and flexible comparison of improvement approaches related to financial evaluation. Energy cost allocation maps created by this model demonstrated the benefits of anaerobic treatment in the WWTP of a soft-drink factory in Japan. Additionally in this WWTP, the observed values of total power consumption were 40% higher than the simulated values, and improvement approaches, such as instrumental control of aeration, were evaluated for their feasibility and financial impact. These results demonstrated the success of the model in adding and reinforcing analytical and predictive functions in the MFCA survey method.
Keywords: Material flow cost accounting | Process simulation model | Industrial wastewater | Energy saving | Food and beverage industry
مقاله انگلیسی
7 Evolving challenges and strategies for fungal control in the food supply chain
چالش ها و استراتژی های در حال کنترل برای کنترل قارچ در زنجیره تامین مواد غذایی-2021
Fungi that spoil foods or infect crops can have major socioeconomic impacts, posing threats to food security. The strategies needed to manage these fungi are evolving, given the growing incidence of fungicide resistance, tightening regulations of chemicals use and market trends imposing new food-preservation challenges. For example, alternative methods for crop protection such as RNA-based fungicides, biocontrol, or stimulation of natural plant defences may lessen concerns like environmental toxicity of chemical fungicides. There is renewed focus on natural product preservatives and fungicides, which can bypass regulations for ‘clean label’ food products. These require investment to find effective, safe activities within complex mixtures such as plant extracts. Alternatively, physical measures may be one key for fungal control, such as polymer materials which passively resist attachment and colonization by fungi. Reducing or replacing traditional chlorine treatments (e.g. of post-harvest produce) is desirable to limit formation of disinfection by-products. In addition, the current growth in lower sugar food products can alter metabolic routing of carbon utilization in spoilage yeasts, with implications for efficacy of food preservatives acting via metabolism. The use of preservative or fungicide combinations, while involving more than one chemical, can reduce total chemicals usage where these act synergistically. Such approaches might also help target different subpopulations within heteroresistant fungal populations. These approaches are discussed in the context of cur- rent challenges for food preservation, focussing on pre-harvest fungal control, fresh pro- duce and stored food preservation. Several strategies show growing potential for mitigating or reversing the risks posed by fungi in the food supply chain.ª 2021 The Author(s). Published by Elsevier Ltd on behalf of British Mycological Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). * Corresponding author. E-mail address: Simon.Avery@nottingham.ac.uk (S. V. Avery).1 Authors made equal contributions. https://doi.org/10.1016/j.fbr.2021.01.0031749-4613/ª 2021 The Author(s). Published by Elsevier Ltd on behalf of British Mycological Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Agrichemicals | Antimicrobial resistance | Food spoilage | Phytopathogens | Spoilage fungi
مقاله انگلیسی
8 Mapping global fuel cell vehicle industry chain and assessing potential supply risks
نقشه برداری از زنجیره صنعت خودروی سوختی سوخت جهانی و ارزیابی خطرات احتمالی تأمین-2021
Fuel cell vehicles (FCVs) have the potential to contribute significantly to improving air quality and addressing climate concerns in the future. However, due to the highly dynamic technology and manufacturing developments, there is a lack of understanding of the state- of-the-art global FCV industry chain and associated supply risks. This study fills such a research gap by mapping global FCV industry chain during the period 2017e2019, and assessing the supply risks of relevant key commodities. The results show that significant supply risks existed in global FCV industry chain, especially in upstream commodities like platinum and gas diffusion layer (GDL). The combined indicator of Herfindahl-Hirschman Index and Worldwide Governance-Indicator (HHI-WGI) is used to quantify the supply risks, showing that HHI-WGI of platinum is on the highest level. On the national level, supply risks are identified primarily in platinum for Japan, in vehicles for the United States, and along the entire industry chain for China. Network analysis is conducted to visualize and analyze how countries, companies and commodities are connected, showing that the highest supply risks were identified in GDLs. It is recommended that country-specific measures should be taken to mitigate supply risks, including building up national stocks of critical materials, investing overseas, enhancing the guidance over industry policies, and stepping up infrastructure construction.© 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.* Corresponding author. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China. E-mail address: hao@tsinghua.edu.cn (H. Hao).https://doi.org/10.1016/j.ijhydene.2021.02.0410360-3199/© 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Keywords: Fuel cell vehicle | Industry chain | Supply risk | Network analysis
مقاله انگلیسی
9 Automatic Identification of Abaca Bunchy Top Disease using Deep Learning Models
121-S1877050921000132-2021
Usage of computer vision and artificial intelligence in the detection and identification of plant diseases has been explored and utilized in agricultural crops and had proven to perform efficiently. However, this disease detection and identification technology has not yet being explored and examined for some economically valuable crops like abaca and banana. This study intended to develop an automatic identification system for Abaca Bunchy Top Disease (ABTD) using different deep learning models. The study utilized a total of 3,840 petioles and petioles with leaves images taken using DSLR and mobile camera. Selected and pre- processed images were then subjected to augmentation techniques, normalization techniques, and morphometric and geometric analyses. Images were then trained using AlexNet, ZFNet, VGG16, and VGG19 architectures and the results were evaluated using Confusion Matrix in terms of accuracy, error rate, and precision. DSLR captured images on leaves and petioles with leav es showed an accuracy greater than 90% in all architectures except VGG16 with only 83% accuracy, while on mobile captured images, leaves showed above 90% accuracy compared to other groups. As to precision, DSLR captured images on petioles showed that out of four architectures, two models showed above 90% precision except for AlexNet and VGG16. However, for mobile captured images, three models showed above 90% precision using petioles image except VGG16. Furthermore, the models can be used for development of software application for detection, monitoring, and evaluation of ABTD.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 5th International Conference on Computer Science and Computational Intelligence 2020
Keywords: Abaca Bunchy Top Disease | Computer Vision | Neural Network | Deep Learning
مقاله انگلیسی
10 Optimization of Underwater Marker Detection Based on YOLOv3
بهینه سازی تشخیص نشانگر زیر آب بر اساس YOLOv3-2021
The research on the detection and recognition technology of marker is of great significance for some underwater operations, such as marine resource exploration, underwater robot operation and so on. The existing image processing methods can effectively detect and recognize the markers in the air. Nevertheless, in the underwater environment, the complex imaging environment of the ocean leads to serious degradation of underwater images obtained by the optical vision system. Due to the lack of effective information for object recognition, the severely degraded underwater images increases the difficulty of detection and recognition of underwater objects. With the development of high-tech underwater imaging equipment, the quality of underwater images has been improved to a certain extent, but there are still some phenomena such as color fading, low contrast and blurred details. Solutions to overcome these problems are important for the exploration of the ocean. In this paper, we introduce a deep learning model to optimize the performance of detection, and make a unique marker dataset for the application scene of our experiment. We first use the deep learning network to pre-train the marker images in the air. Next, we use the underwater marker images for fine-tuning. Finally, after the target marker is detected, the traditional image processing method is used to recognize the marker. Experimental results show that the optimization method we proposed achieves better performance on the dataset.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.
Keywords: Computer vision | Underwater marker detection | Deep learning
مقاله انگلیسی
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