scholarly journals A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fariba Rezaei ◽  
Mehran Yazdi
Keyword(s):  
Anomaly Detection ◽  
Topic Model ◽  
Nonnegative Matrix ◽  
Feature Representation ◽  
Test Time ◽  
Model Learning ◽  
Deep Model ◽  
Level Information ◽  
The Difference ◽  
High Level

Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.

scholarly journals Bidirectional Nonnegative Deep Model and Its Optimization in Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xianhua Zeng ◽  
Zhengyi He ◽  
Hong Yu ◽  
Shengwei Qu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Nonnegative Matrix ◽  
Feature Representation ◽  
Nonlinear Fitting ◽  
Feature Representations ◽  
Deep Model ◽  
Proposed Model ◽  
Out Of Sample ◽  
Narrow Signal

Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

scholarly journals Cascaded Cross-Layer Fusion Network for Pedestrian Detection

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 139
Author(s):  
Zhifeng Ding ◽  
Zichen Gu ◽  
Yanpeng Sun ◽  
Xinguang Xiang
Keyword(s):  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Performance ◽  
Feature Representation ◽  
Cross Layer ◽  
Feature Maps ◽  
Level Information ◽  
High Level ◽  
Center Map ◽  
The Impact

The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.

scholarly journals A Novel Region-Extreme Convolutional Neural Network for Melanoma Malignancy Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Nudrat Nida ◽  
Aun Irtaza ◽  
Muhammad Haroon Yousaf
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Image Analysis ◽  
Feature Representation ◽  
Machine Learning Techniques ◽  
Feature Maps ◽  
Model Learning ◽  
Learning Machine ◽  
Elm Classifier ◽  
High Level

Melanoma malignancy recognition is a challenging task due to the existence of intraclass similarity, natural or clinical artefacts, skin contrast variation, and higher visual similarity among the normal or melanoma-affected skin. To overcome these problems, we propose a novel solution by leveraging “region-extreme convolutional neural network” for melanoma malignancy recognition as malignant or benign. Recent works on melanoma malignancy recognition employed the traditional machine learning techniques based on various handcrafted features or the recently introduced CNN network. However, the efficient training of these models is possible, if they localize the melanoma affected region and learn high-level feature representation from melanoma lesion to predict melanoma malignancy. In this paper, we incorporate this observation and propose a novel “region-extreme convolutional neural network” for melanoma malignancy recognition. Our proposed region-extreme convolutional neural network refines dermoscopy images to eliminate natural or clinical artefacts, localizes melanoma affected region, and defines precise boundary around the melanoma lesion. The defined melanoma lesion is used to generate deep feature maps for model learning using the extreme learning machine (ELM) classifier. The proposed model is evaluated on two challenge datasets (ISIC-2016 and ISIC-2017) and performs better than ISIC challenge winners. Our region-extreme convolutional neural network recognizes the melanoma malignancy 85% on ISIC-2016 and 93% on ISIC-2017 datasets. Our region-extreme convolutional neural network precisely segments the melanoma lesion with an average Jaccard index of 0.93 and Dice score of 0.94. The region-extreme convolutional neural network has several advantages: it eliminates the clinical and natural artefacts from dermoscopic images, precisely localizes and segments the melanoma lesion, and improves the melanoma malignancy recognition through feedforward model learning. The region-extreme convolutional neural network achieves significant performance improvement over existing methods that makes it adaptable for solving complex medical image analysis problems.

Difference of [Ca2+]i Movements in Platelets Stimulated by Thrombin and TRAP: the Involvement of αIIbβ3-Mediated TXA2 Synthesis

1998 ◽  
Vol 79 (06) ◽  
pp. 1184-1190 ◽  
Author(s):  
Yoshiaki Tomiyama ◽  
Shigenori Honda ◽  
Kayoko Senzaki ◽  
Akito Tanaka ◽  
Mitsuru Okubo ◽  
...  
Keyword(s):  
Platelet Aggregation ◽  
Fibrinogen Binding ◽  
Adp Release ◽  
The Difference ◽  
Txa2 Receptor Antagonist ◽  
High Level ◽  
Inhibition Studies ◽  
Peak Increase ◽  
Second Peak ◽  
Assay Conditions

SummaryThis study investigated the difference of [Ca2+]i movement in platelets in response to thrombin and TRAP. The involvement of αIIbβ3 in this signaling was also studied. Stimulation of platelets with thrombin at 0.03 U/ml caused platelet aggregation and a two-peak increase in [Ca2+]i. The second peak of [Ca2+]i, but not the first peak was abolished by the inhibition of platelet aggregation with αIIbβ3 antagonists or by scavenging endogenous ADP with apyrase. A cyclooxygenase inhibitor, aspirin, and a TXA2 receptor antagonist, BM13505, also abolished the second peak of [Ca2+]i but not the first peak, although these regents did not inhibit aggregation. Under the same assay conditions, measurement of TXB2 demonstrated that αIIbβ3 antagonists and aspirin almost completely inhibited the production of TXB2. In contrast to thrombin-stimulation, TRAP caused only a single peak of [Ca2+]i even in the presence of platelet aggregation, and a high level of [Ca2+]i increase was needed for the induction of platelet aggregation. The inhibition of aggregation with αIIbβ3 antagonists had no effect on [Ca2+]i change and TXB2 production induced by TRAP. Inhibition studies using anti-GPIb antibodies suggested that GPIb may be involved in the thrombin response, but not in the TRAP. Our findings suggest that low dose thrombin causes a different [Ca2+]i response and TXA2 producing signal from TRAP. Endogenous ADP release and fibrinogen binding to αIIbβ3 are responsible for the synthesis of TXA2 which results in the induction of the second peak of [Ca2+]i in low thrombin- but not TRAP-stimulated platelets.

ATTITUDE TO OPEN ACCESS IN RUSSIAN SCHOLARLY COMMUNITY: 2018. SURVEY RESULTS AND ANALYSIS

2018 ◽  
Vol 1 (1) ◽  
pp. 6-21 ◽  
Author(s):  
I. K. Razumova ◽  
N. N. Litvinova ◽  
M. E. Shvartsman ◽  
A. Yu. Kuznetsov
Keyword(s):  
Open Access ◽  
Open Access Publishing ◽  
Scholarly Community ◽  
Research Areas ◽  
Survey Results ◽  
The Difference ◽  
Policies And Programs ◽  
The Uk ◽  
High Level ◽  
Access Policies

Introduction. The paper presents survey results on the awareness towards and practice of Open Access scholarly publishing among Russian academics.Materials and Methods. We employed methods of statistical analysis of survey results. Materials comprise results of data processing of Russian survey conducted in 2018 and published results of the latest international surveys. The survey comprised 1383 respondents from 182 organizations. We performed comparative studies of the responses from academics and research institutions as well as different research areas. The study compares results obtained in Russia with the recently published results of surveys conducted in the United Kingdom and Europe.Results. Our findings show that 95% of Russian respondents support open access, 94% agree to post their publications in open repositories and 75% have experience in open access publishing. We did not find any difference in the awareness and attitude towards open access among seven reference groups. Our analysis revealed the difference in the structure of open access publications of the authors from universities and research institutes. Discussion andConclusions. Results reveal a high level of awareness and support to open access and succeful practice in the open access publications in the Russian scholarly community. The results for Russia demonstrate close similarity with the results of the UK academics. The governmental open access policies and programs would foster the practical realization of the open access in Russia.

Quantitative indicators of air traffic controllers’ attitude to the danger of errors

2020 ◽  
pp. 68-78
Author(s):  
O. M. Reva ◽  
V. V. Kamyshin ◽  
S. P. Borsuk ◽  
V. A. Shulhin ◽  
A. V. Nevynitsyn
Keyword(s):  
Human Factor ◽  
Differential Method ◽  
Air Traffic ◽  
Professional Activity ◽  
Aviation Accidents ◽  
Air Traffic Controllers ◽  
The Difference ◽  
Level Of Significance ◽  
The Individual ◽  
High Level

The negative and persistent impact of the human factor on the statistics of aviation accidents and serious incidents makes proactive studies of the attitude of “front line” aviation operators (air traffic controllers, flight crewmembers) to dangerous actions or professional conditions as a key component of the current paradigm of ICAO safety concept. This “attitude” is determined through the indicators of the influence of the human factor on decision-making, which also include the systems of preferences of air traffic controllers on the indicators and characteristics of professional activity, illustrating both the individual perception of potential risks and dangers, and the peculiarities of generalized group thinking that have developed in a particular society. Preference systems are an ordered (ranked) series of n = 21 errors: from the most dangerous to the least dangerous and characterize only the danger preference of one error over another. The degree of this preference is determined only by the difference in the ranks of the errors and does not answer the question of how much time one error is more dangerous in relation to another. The differential method for identifying the comparative danger of errors, as well as the multistep technology for identifying and filtering out marginal opinions were applied. From the initial sample of m = 37 professional air traffic controllers, two subgroups mB=20 and mG=7 people were identified with statisti-cally significant at a high level of significance within the group consistency of opinions a = 1%. Nonpara-metric optimization of the corresponding group preference systems resulted in Kemeny’s medians, in which the related (middle) ranks were missing. Based on these medians, weighted coefficients of error hazards were determined by the mathematical prioritization method. It is substantiated that with the ac-cepted accuracy of calculations, the results obtained at the second iteration of this method are more ac-ceptable. The values of the error hazard coefficients, together with their ranks established in the preference systems, allow a more complete quantitative and qualitative analysis of the attitude of both individual air traffic controllers and their professional groups to hazardous actions or conditions.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chih-Hua Tai ◽  
Kuo-Hsuan Chung ◽  
Ya-Wen Teng ◽  
Feng-Ming Shu ◽  
Yue-Shan Chang
Keyword(s):  
Information Fusion ◽  
Mood State ◽  
Fusion Technique ◽  
Level Information ◽  
Information Fusion Technique ◽  
High Level

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

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