Detection-based Approach Research Articles

A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in the Material Science

A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in the Material Science

Real Time Bimodal Emotion Recognition using Hybridized Deep Learning Techniques

Emotion detection has become an important study subject due to the potential uses that it has in many other domains. It incorporates data from various sources, such as facial expressions, voice patterns, physiological indications, and textual signals. To identify human emotions, a real-time bimodal image and audio-based detection-based approach has been proposed. The approach is termed as “bimodal” since images and audio data are considered as inputs to the proposed model. In this study, hybridized CNN-LSTM for the image-based model and Multi-Layer Perceptron is used for the developing audio model. For the image-based model, two combined layers of CNN, and LSTM along with regularization techniques like drop-out, and batch normalization have been adopted for model development. Similarly, for the audio model, the dataset has been fine-tuned using a standard scaler and PCA before training. Based on the combined results of both models, the user’s final emotion is determined. Both the audio-based model and the video-based model outperform the existing models discussed in the literature with higher performance accuracy of 79.34% and 77.27% respectively.

Cyber Security within Smart Cities: A Comprehensive Study and a Novel Intrusion Detection-Based Approach

Cyber Security within Smart Cities: A Comprehensive Study and a Novel Intrusion Detection-Based Approach

Identification of the normative use of medical protective equipment by fusion of object detection and keypoints detection

Background and ObjectiveIrregular use of personal protective equipment (PPE) seriously affects the occupational health and safety of healthcare workers, especially in large public health emergencies such as COVID-19. The danger often occurs in some complex scenarios where the use of certain equipment does not comply with the spatial rules it specifies. The single object detection-based method is difficult to effectively verify whether the worker's use of PPE is normative. Also detecting the use of six classes of PPE brings a large computational load to the task. MethodsIn this paper, we proposed an identification approach that combined human keypoints detection with deep learning object detection to help facilitate the monitoring of healthcare workers' standard PPE use. We used YOLOv4 as the baseline model for PPE detection and MobileNetv3 as the backbone of the detector to reduce the computational effort. In addition, High-Resolution Net (HRNet) was the benchmark for keypoints detection, characterizing the coordinates of 25 key points in the human body. Generalized Intersection over Union (GIoU) was used to establish the association between PPEs and key points, and by calculating the matching score between a PPE and the corresponding body bounding boxes, the authenticity of the PPE specification could be effectively inferred. ResultsThe proposed detector is able to identify whether a healthcare worker is normatively using multiple equipment with a higher precision (95.81 %), recall (96.38 %), and F1-score (96.09 %). Meanwhile, the number of parameters (2.87 M) and the size of the model (6.4 MB) are also more lightweight than other comparative detectors. ConclusionsOur approach is more reliable for reasoning about the normality of personal protection for healthcare workers in some complex scenarios than a single object detection-based approach. The developed identification framework provides a new automated monitoring solution for protection management in healthcare, and the modular design brings more flexible applications for different medical operation scenarios.

Event-Based State Estimation Against Deception Attacks: A Detection-Based Approach

Event-Based State Estimation Against Deception Attacks: A Detection-Based Approach

Measurement of displacement and top beam attitude angle of advanced hydraulic support based on visual detection

We constructed an advanced hydraulic support test bench and proposed a visual detection-based approach for measuring the displacement and attitude angle of the advanced hydraulic support. Firstly, our methodology involved using a binocular camera for image acquisition and stereo rectification of the advanced hydraulic support. Then, we employed the ORB algorithm to detect feature points and the SGBM matching algorithm to determine the disparity value. Finally, the attitude angle solution algorithm and the triangulation method are written to calculate the support displacement and the top beam attitude angle. Results showed that the advanced hydraulic support had an average absolute error of 8 mm in displacement measurement and 0.4° in attitude angle measurement, meeting the accuracy requirements for fully mechanized mining face operations.

A Deep Learning Approach for Meibomian Gland Appearance Evaluation

To develop and evaluate a deep learning algorithm for Meibomian gland characteristics calculation. Evaluation of diagnostic technology. A total of 1616 meibography images of both the upper (697) and lower (919) eyelids from a total of 282 individuals. Images were collected using the LipiView II device. All the provided data were split into 3 sets: the training, validation, and test sets. Data partitions used proportions of 70/10/20% and included data from 2 optometry settings. Each set was separately partitioned with these proportions, resulting in a balanced distribution of data from both settings. The images were divided based on patient identifiers, such that all images collected for one participant could end up only in one set. The labeled images were used to train a deep learning model, which was subsequently used for Meibomian gland segmentation. The model was then applied to calculate individual Meibomian gland metrics. Interreader agreement and agreement between manual and automated methods for Meibomian gland segmentation were also carried out to assess the accuracy of the automated approach. Meibomian gland metrics, including length ratio, area, tortuosity, intensity, and width, were measured. Additionally, the performance of the automated algorithms was evaluated using the aggregated Jaccard index. The proposed semantic segmentation-based approach achieved average aggregated Jaccard index of mean 0.4718 (95% confidence interval [CI], 0.4680-0.4771) for the 'gland' class and a mean of 0.8470 (95% CI, 0.8432-0.8508) for the 'eyelid' class. The result for object detection-based approach was a mean of 0.4476 (95% CI, 0.4426-0.4533). Both artificial intelligence-based algorithms underestimated area, length ratio, tortuosity, widthmean, widthmedian, width10th, and width90th. Meibomian gland intensity was overestimated by both algorithms compared with the manual approach. The object detection-based algorithm seems to be as reliable as the manual approach only for Meibomian gland width10th calculation. The proposed approach can successfully segment Meibomian glands; however, to overcome problems with gland overlap and lack of image sharpness, the proposed method requires further development. The study presents another approach to utilizing automated, artificial intelligence-based methods in Meibomian gland health assessment that may assist clinicians in the diagnosis, treatment, and management of Meibomian gland dysfunction. The authors have no proprietary or commercial interest in any materials discussed in this article.

Joint autoencoder-regressor deep neural network for remaining useful life prediction

Ubiquitous availability of IoT technologies allows processing of large amounts of data to improve prognostics tasks in industrial applications. One such important task of prognostics is the prediction of remaining useful life of a system from past performance data. In practice, although failure points are pinpointed, the actual start of degradation is not necessarily available, but usually modeled simply with a linear degradation assumption. In this paper we present a data-driven approach to remaining useful life prediction using joint autoencoder-regression network, a deep neural network model incorporating a convolutional neural network autoencoder and a long-short term memory network regressor trained end-to-end. We also present a new fault detection-based approach to modeling remaining useful life degradation. This model allows a better estimate of the start and progress of equipment degradation ending with a failure. We demonstrate the effectiveness of the proposed algorithms on two datasets. The first one is C-MAPSS frequently used as a benchmark among prognostic researchers. The second one is PHME20, a recent prognostic dataset from a prognostics competition. These experiments show that the proposed algorithms are capable of predicting remaining useful life as good as the state of art methods. The results also show that fault detection-based labeling outperforms linear labeling.

Disease Detection of Solanaceous Crops Using Deep Learning for Robot Vision

Traditionally, the farmers manage the crops from the early growth stage until the mature harvest stage by manually identifying and monitoring plant diseases, nutrient deficiencies, controlled irrigation, and controlled fertilizers and pesticides. Even the farmers have difficulty detecting crop diseases using their naked eyes due to several similar crop diseases. Identifying the correct diseases is crucial since it can improve the quality and quantity of crop production. With the advent of Artificial Intelligence (AI) technology, all crop-managing tasks can be automated using a robot that mimics a farmer's ability. However, designing a robot with human capability, especially in detecting the crop's diseases in real-time, is another challenge to consider. Other research works are focusing on improving the mean average precision and the best result reported so far is 93% of mean Average Precision (mAP) by YOLOv5. This paper focuses on object detection of the Convolutional Neural Network (CNN) architecture-based to detect the disease of solanaceous crops for robot vision. This study's contribution involved reporting the developmental specifics and a suggested solution for issues that appear along with the conducted study. In addition, the output of this study is expected to become the algorithm of the robot's vision. This study uses images of four crops (tomato, potato, eggplant, and pepper), including 23 classes of healthy and diseased crops infected on the leaf and fruits. The dataset utilized combines the public dataset (PlantVillage) and self-collected samples. The total dataset of all 23 classes is 16580 images divided into three parts: training set, validation set, and testing set. The dataset used for training is 88% of the total dataset (15000 images), 8% of the dataset performed a validation process (1400 images), and the rest of the 4% dataset is for the test process (699 images). The performances of YOLOv5 were more robust in terms of 94.2% mAP, and the speed was slightly faster than Scaled-YOLOv4. This object detection-based approach has proven to be a promising solution in efficiently detecting crop disease in real-time.

Improvements for pedestrian safety application P-Minder

P-Minder is a lightweight sidewalk segmentation and obstacle detection-based approach designed for phubbers’ safety, which can help phubbers to avoid obstacles and other dangers during phubbing walking. But two issues have been found in previous experiments: P-Minder undifferentiated scores the recognition results and costs high resource occupation during background operation. Therefore, this paper proposes the sector correlation model to classify the recognition and segmentation results for an accurate judgment. Then, the new application architecture design and walking detection model decrease the resource occupation. Besides, the datasets are expanded, and the models are retrained to adapt to the improved architecture. In the same datasets, P-Minder achieves a 1.7 percentage point accuracy improvement during the experiment. The experimental result proves that the segmentation model can reach 81.2% mean intersection over union. Besides, the method finally achieves 75.52% detection accuracy in the experiment with a low computing resources cost.

Sensitive mid-infrared photothermal gas detection enhanced by self-heterodyne harmonic amplification of a mode-locked fiber laser probe

In this work, a method of photothermal spectroscopic signal extraction is presented. The refractive index modulation readout is realized in a purely frequency detection-based approach, by demodulating the beatnotes of a mode-locked fiber laser operating at 1.56 µm. A unique and non-complex self-heterodyne harmonic amplification technique is employed, yielding an increase in the limit of detection by a factor of 22. The sensor's performance was evaluated by detecting nitric oxide at 5.26 µm, confirming the feasibility of separating the pump and probe sections of the device. The sensor reached a detection limit of 9.6 parts-per-billion by volume for an integration time of 136 s, with only a 20 cm-long laser-gas molecules interaction path length.

MAMask: Multi‐feature aggregation instance segmentation with pyramid attention mechanism

Instance segmentation is a fundamental yet challenging vision task. Recently, many instance segmentation methods have attempted to use attention mechanisms to improve model efficiency. However, these methods still ignore the problem of information loss in lateral connection of Feature Pyramid Networks (the supplement operation of low-resolution, semantically strong features in FPN). The paper presents an effective detection-based approach named MAMask, which is closely tied to the one-stage method, Fully Convolutional One-Stage Object Detection (FCOS). In particular, it adopts the multi-feature aggregation decoder with pyramid integrate attention (PIA) to instance segmentation. The pyramid integrate attention block can prevent the loss of important channel information by learning richer multi-scale representation. Meanwhile, it also brings significant improvements in performance for existing FPN-based frameworks at slight additional computational costs. The proposed MAMask achieves 37.3% in box AP on the COCO dataset. The method outperforms a few recent methods without longer training time. Compared with the current typical algorithms, the proposed method has achieved excellent performance.

Anomalous behavior detection-based approach for authenticating smart home system users.

This paper presents Duenna, an authentication framework for smart home systems (SHSs). When using controlling apps (e.g., a smartphone app), Duenna makes sure that only legitimate SHS users are allowed to operate their Internet of things (IoT) devices. Duenna is built upon a behavioral anomaly detection (BAD)-based approach. In particular, we hypothesize that SHS users usually operate their home IoT devices in typical and distinctive patterns. Therefore, users that attempt to operate devices differently from such a regular behavior are considered malicious. Technically, Duenna operates in two modes. In an initialization operation, Duenna first collects and processes the historical cyber and physical activities of an SHS user in addition to the historical states of the SHS itself to build a set of incremental anomaly detection (AD) models. Then, in an interactive operation, the trained AD models are, then, used as a baseline from which anomalous commands (i.e., outliers) are detected and rejected, while regular commands (i.e., targets) are considered legitimate and allowed to be executed. Through an empirical evaluation conducted on real-world data, Duenna exhibits high authentication rates ensuring both security and user experience. The findings obtained from such evaluation show that a user behavior-based approach is a promising security scheme that could be integrated into existing SHS platforms.

Novelty detection-based approach for Alzheimer\u2019s disease and mild cognitive impairment diagnosis from EEG

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

A Study of Timing Side-Channel Attacks and Countermeasures on JavaScript and WebAssembly

Side-channel attacks are a group of powerful attacks in hardware security that exploit the deficiencies in the implementation of systems. Timing side-channel attacks are one of the main side-channel attack categories that use the time difference of running an operation in different states. Many powerful attacks can be classified into this type of attack, including cache attacks. The limitation of these attacks is the need to run the spy program on the victim's system. Various studies have tried to overcome this limitation by implementing these attacks remotely on JavaScript and WebAssembly. This paper provides the first comprehensive evaluation of timing side-channel attacks on JavaScript and investigates challenges and countermeasures to overcome these attacks. Moreover, by investigating the countermeasures and their strengths and weaknesses, we introduce a detection-based approach, called Lurking Eyes. Our approach has the least reduction in the performance of JavaScript and WebAssembly. The evaluation results show that the Lurking eyes have an accuracy of 0.998, precision of 0.983, and F-measure of 0.983. Considering these values and no limitations, this method can be introduced as an effective way to counter timing side-channel attacks on JavaScript and WebAssembly. Also, we provide a new accurate timer, named Eagle timer, based on WebAssembly memory for implementing these attacks.

Cross-linguistic influence on the use of L2 collocations: the case of Vietnamese learners

AbstractUsing collocation is a key part of second language ability (Granger, Sylviane. 2018. Formulaic sequences in learner corpora: Collocations and lexical bundles. In Anna Siyanova-Chanturia & Ana Pellicer-Sanchez (eds.),Understanding formulaic language: A second language acquisition perspective, 228–247. New York: Routledge; Nattinger, James R. & Jeamette S. DeCarrico. 1992.Lexical phrases and language teaching. Oxford: Oxford University Press; Nesselhauf, Nadja. 2004.Collocations in a learner corpus. Amsterdam; Philadelphia: J. Benjamins Pub. Co.; Pawley, Andrew & Frances H. Syder. 1983. Two puzzles for linguistics: Nativelike selection and nativelike fluency. In Jack Richards & Richard W. Schmidt (eds.),Language and communication, 191–228. London: Longman). Researchers often hypothesize that the influence of the first language is an important factor in the production and understanding of unconventional collocations (Huang, Li-Shi. 2001. Knowledge of English collocations: An analysis of Taiwanese EFL learners.Paper presented at the Texas Foreign Languguage Education Conference, Texas; Laufer, Bhatia & Tina Waldman. 2011. Verb-noun collocations in second language writing: A corpus analysis of learners’ English.Language Learning61(2). 647–672.https://doi.org/10.1111/j.1467-9922.2010.00621.x; Phoocharoensil, Supakorn. 2013. Cross-linguistic influence: Its impact on L2 English collocation production.English Language Teaching6(1). 1–10) but we are only now starting to understand this. The present study provides a robust investigation of cross-linguistic influences by exploring how Vietnamese influenced Vietnamese learners’ use of English language verb-noun and adjective noun collocations in 104 350-word argumentative essays, using a framework derived from Jarvis, Scott. 2012. The detection-based approach: An overview. In S. Jarvis & S. A. Crossley (eds.),Approaching language transfer through text classification: Explorations in the detection-based approach, 1st ed., Vol. 64, 1–33. Bristol: Multilingual Matters, drawing on homogeneity among speakers of Vietnamese; heterogeneity between users of Vietnamese and other language; and formal and conceptual congruity between collocations learners produce in English and equivalent terms in Vietnamese. The study found that less than 10% of the collocations learners produced were unconventional and of these, 40% of collocations were influenced by the first language (L1); errors associated with incorrect use of prepositions in verb-noun collocations (e.g. the addition, omission or misuse of prepositions) are strongly L1-motivated. Learners make errors with not only incongruent collocations (collocations with no direct L1 equivalents) but also with congruent collocations (collocations with direct L1 translation).

Improving Distant Supervised Relation Extraction with Noise Detection Strategy

Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision inevitably suffers from the noisy labeling problem that will severely damage the performance of relation extraction. Currently, most DSRE methods are mainly focused on reducing the weights of noisy sentences, ignoring the bag-level noise where all sentences in a bag are wrongly labeled. In this paper, we present a novel noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels with entity information and dynamically correct them, which can alleviate both instance-level and bag-level noisy problems. By this means, we can extend the dataset from the Web tables without introducing more noise. In this approach, to embed the semantics of sentences from corpus and web tables, we firstly propose a powerful sentence coder that employs an internal multi-head self-attention mechanism between the piecewise max-pooling convolutional neural network. Second, we adopt a noise detection strategy, which is expected to dynamically detect and correct the original noisy label according to the similarity between sentence representation and entity-aware embeddings. Then, we aggregate the information from corpus and web tables to make the final relation prediction. Experimental results on a public benchmark dataset demonstrate that our proposed approach achieves significant improvements over the state-of-the-art baselines and can effectively reduce the noisy labeling problem.

МЕТОДЫ РАСПОЗНАВАНИЯ ОБРАЗОВ В ВИДЕОПОТОКЕ

The article examines the problem of automatic object recognition using a video stream as a digital image. Algorithms for recognizing and tracking objects in the video stream are considered, methods used in video processing are analyzed, and the use of machine learning tools in working with video is described.The main approaches to solving the problem of recognizing moving objects in a video stream are investigated: the detection-based approach and the tracking-based approach. Arguments are made in favor of the tracking-based approach, and, in addition, modern methods of tracking objects in the video stream are considered. In particular, the algorhythms: Online Boosting Tracker - one of the first object tracking algorithms with high tracking accuracy, MIL Tracker (Multiple Instance Learning Tracker), which is a development of the idea of learning with a teacher and the Online Boosting algorithm and the KCF Tracker algorithm (Kernelized Correlation Filters Tracker) - a method that uses the mathematical properties of overlapping areas of positive examples.As a result, the advantages and disadvantages of the considered methods and algorithms for recognizing and tracking objects for various applications are highlighted.

An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses

Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019–2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

GPS spoofing‐based time synchronisation attack in advanced metering infrastructure and its protection

Advanced metering infrastructure (AMI) plays a key role in power systems. Since smart meters and meter collectors are synchronised to the time synchronisation devices (TSDs) in the head end system (HES) of AMI, they are vulnerable to global positioning system (GPS) spoofing-based time synchronisation attack (TSA). Impacts of GPS spoofing-based TSA on AMI are investigated in this study. It is uncovered that, since AMI is a distributed networked system and metering data and control commands transmitted in AMI could be of large latency, data and commands with large latency over the specific threshold are considered to be invalid according to validity verification mechanism of average distributed system. Therefore, the disorder in time synchronisation induced by GPS spoofing-based TSA could disable functions of HES of AMI, such as meter reading and remote control. A time jitter detection-based approach is developed to identify and prevent from GPS spoofing-based TSA. A high-precision oven-controlled crystal oscillator with cumulative error compensation is utilised to identify time jitter of the satellite clock and help ride through sustained GPS spoofing-based TSA. Simulation on FPGA demonstrates the effectiveness of the proposed approach.