scholarly journals Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 531
Author(s):  
Seung-Cheol Baek ◽  
Jae Ho Chung ◽  
Yoonseob Lim
Keyword(s):  
Hearing Aids ◽  
Real Life ◽  
Auditory Attention ◽  
Detection Accuracy ◽  
Neurofeedback Training ◽  
Neural Signals ◽  
Detection Model ◽  
Proposed Model ◽  
Online Implementation ◽  
Listening Experiment

Auditory attention detection (AAD) is the tracking of a sound source to which a listener is attending based on neural signals. Despite expectation for the applicability of AAD in real-life, most AAD research has been conducted on recorded electroencephalograms (EEGs), which is far from online implementation. In the present study, we attempted to propose an online AAD model and to implement it on a streaming EEG. The proposed model was devised by introducing a sliding window into the linear decoder model and was simulated using two datasets obtained from separate experiments to evaluate the feasibility. After simulation, the online model was constructed and evaluated based on the streaming EEG of an individual, acquired during a dichotomous listening experiment. Our model was able to detect the transient direction of a participant’s attention on the order of one second during the experiment and showed up to 70% average detection accuracy. We expect that the proposed online model could be applied to develop adaptive hearing aids or neurofeedback training for auditory attention and speech perception.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals EEG-Based Auditory Attention Detection and Its Possible Future Applications for Passive BCI

2021 ◽  
Vol 3 ◽  
Author(s):  
Joan Belo ◽  
Maureen Clerc ◽  
Daniele Schön
Keyword(s):  
Hearing Aids ◽  
Auditory Attention ◽  
Noisy Environment ◽  
Neural Signals ◽  
Interactive Music ◽  
Efficient Communication ◽  
Potential Applications ◽  
Research Findings ◽  
Fundamental Research ◽  
Multi Speech

The ability to discriminate and attend one specific sound source in a complex auditory environment is a fundamental skill for efficient communication. Indeed, it allows us to follow a family conversation or discuss with a friend in a bar. This ability is challenged in hearing-impaired individuals and more precisely in those with a cochlear implant (CI). Indeed, due to the limited spectral resolution of the implant, auditory perception remains quite poor in a noisy environment or in presence of simultaneous auditory sources. Recent methodological advances allow now to detect, on the basis of neural signals, which auditory stream within a set of multiple concurrent streams an individual is attending to. This approach, called EEG-based auditory attention detection (AAD), is based on fundamental research findings demonstrating that, in a multi speech scenario, cortical tracking of the envelope of the attended speech is enhanced compared to the unattended speech. Following these findings, other studies showed that it is possible to use EEG/MEG (Electroencephalography/Magnetoencephalography) to explore auditory attention during speech listening in a Cocktail-party-like scenario. Overall, these findings make it possible to conceive next-generation hearing aids combining customary technology and AAD. Importantly, AAD has also a great potential in the context of passive BCI, in the educational context as well as in the context of interactive music performances. In this mini review, we firstly present the different approaches of AAD and the main limitations of the global concept. We then expose its potential applications in the world of non-clinical passive BCI.

scholarly journals Webshell Detection Based on Executable Data Characteristics of PHP Code

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zulie Pan ◽  
Yuanchao Chen ◽  
Yu Chen ◽  
Yi Shen ◽  
Xuanzhen Guo
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Remote Access ◽  
Detection Accuracy ◽  
Controlled Experiments ◽  
Data Set ◽  
Detection Model ◽  
Proposed Model ◽  
Text Features ◽  
And Control

A webshell is a malicious backdoor that allows remote access and control to a web server by executing arbitrary commands. The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. The key idea is to combine the executable data characteristics of the PHP code with static text features for webshell classification. To verify the proposed model, we construct a cleaned data set of webshell consisting of 2,917 samples from 17 webshell collection projects and conduct extensive experiments. We have designed three sets of controlled experiments, the results of which show that the accuracy of the three algorithms has reached more than 99.40%, the highest reached 99.66%, the recall rate has been increased by at least 1.8%, the most increased by 6.75%, and the F1 value has increased by 2.02% on average. It not only confirms the efficiency of the grammatical features in webshell detection but also shows that our system significantly outperforms several state-of-the-art rivals in terms of detection accuracy and recall rate.

scholarly journals An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

2021 ◽  
Author(s):  
G Manoharan ◽  
K Sivakumar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Outlier Detection ◽  
Hidden Markov ◽  
Research Work ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Huge Data ◽  
Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

scholarly journals A Machine Learning Approach to Fitting Prescription for Hearing Aids

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 736
Author(s):  
Mondol ◽  
Lee
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Real Life ◽  
Training Data ◽  
Prescription Database ◽  
Data Set ◽  
Promising Tool ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Two Phases

A successful Hearing-Aid Fitting (HAF) is more than just selecting an appropriate HearingAid (HA) device for a patient with Hearing Loss (HL). The initial fitting is given by the prescriptionbased on user’s hearing loss; however, it is often necessary for the audiologist to readjust someparameters to satisfy the user demands. Therefore, in this paper, we concentrated on a new applicationof Neural Network (NN) combined with a Transfer Learning (TL) strategy to develop a fittingalgorithm with the prescription database for hearing loss and readjusted gain to minimize the gapbetween fitting satisfaction. As prior information, we generated the data set from two popularhearing-aid fitting software, then fed the training data to our proposed model, and verified theperformance of the architecture. Pondering real life circumstances, where numerous fitting recordsmay not always be accessible, we first investigated the number of minimum fitting records requiredfor possible sufficient training. After that, we evaluated the performance of the proposed algorithmin two phases: (a) NN with refined hyper parameter showed enhanced performance in compareto state-of-the-art DNN approach, and (b) the TL approach boosted the performance of the NNalgorithm in a broad way. Altogether, our model provides a pragmatic and promising tool for HAF.

scholarly journals A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hongchao Song ◽  
Zhuqing Jiang ◽  
Aidong Men ◽  
Bo Yang
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Real Life ◽  
Detection Methods ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Detection Model ◽  
Anomaly Detector

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

scholarly journals Voice activity detection in noisy conditions using tiny convolutional neural network

Informatics ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 36-43
Author(s):  
R. S. Vashkevich ◽  
E. S. Azarov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Voice Activity Detection ◽  
Detection Accuracy ◽  
Activity Detection ◽  
Detection Model ◽  
Proposed Model ◽  
Computational Resources ◽  
Voice Activity

The paper investigates the problem of voice activity detection from a noisy sound signal. An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. Proposed model doesn’t require a lot of computational resources that allows to use it as part of the “internet of things” concept for compact low power devices. At the same time the model provides state of the art results in voice activity detection in terms of detection accuracy. The properties of the model are achieved by using a special convolutional layer that considers the harmonic structure of vocal speech. This layer also eliminates redundancy of the model because it has invariance to changes of fundamental frequency. The model performance is evaluated in various noise conditions with different signal-to-noise ratios. The results show that the proposed model provides higher accuracy compared to voice activity detection model from the WebRTC framework by Google.

scholarly journals Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-21
Author(s):  
Md Abdullah Al Hafiz Khan ◽  
Nirmalya Roy ◽  
H. M. Sajjad Hossain
Keyword(s):  
Large Scale ◽  
Wearable Sensors ◽  
Real Life ◽  
Estimation Algorithm ◽  
Detection Accuracy ◽  
Detection Model ◽  
Localization Scheme ◽  
Occupancy Detection ◽  
Multimodal Data Fusion ◽  
Natural Settings

Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.

scholarly journals An Improved YOLOv2 for Vehicle Detection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4272 ◽  
Author(s):  
Jun Sang ◽  
Zhongyuan Wu ◽  
Pei Guo ◽  
Haibo Hu ◽  
Hong Xiang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Vehicle Detection ◽  
Training Dataset ◽  
Validation Dataset ◽  
Detection Accuracy ◽  
Extraction Ability ◽  
Detection Model ◽  
Vehicle Type ◽  
Proposed Model ◽  
Bounding Boxes

Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the “vehicle face” is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability.

scholarly journals Automatic Bridge Crack Detection Using a Convolutional Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2867 ◽  
Author(s):  
Hongyan Xu ◽  
Xiu Su ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
Kerang Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Transportation Safety ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Convolutional Network ◽  
Detection Model ◽  
Proposed Model ◽  
Spatial Pyramid Pooling

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

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