Reviewerly/exHarmony huggingface dataset:
Adaptive Data Augmentation for Deep Receivers

Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled data into a larger data set for training deep receivers without transmitting more pilots. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose a dedicated augmentation scheme for exploiting the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Our method models each symbols class as Gaussian, using the available data to estimate its moments, while possibly leveraging data corresponding to related statistical models, e.g., past channel realizations, to improve the estimate. The estimated clusters are used to enrich the data set by generating new samples used for training the DNN. The superiority of our approach is numerically evaluated for training a deep receiver on a linear and non-linear synthetic channels, as well as a COST 2100 channel. We show that our augmentation allows DNN-aided receivers to achieve gain of up to 3dB in bit error rate, compared to regular non-augmented training.
Image classification based on multi-granularity convolutional Neural network model

In the field of image classification, traditional feature extraction algorithms, such as texture feature, local feature and global feature, more or less lose some important image classification information, leading to the reduction of the classification effect. Deep learning based on feature pyramid can identify objects of different scales, but it will greatly increase the density of computation and storage. Therefore, we propose a convolutional neural network image classification method based on multi-granularity features. The convolutional neural network model consists of three different channels, each channel uses different granularity convolution kernels to extract multi-granularity feature information, and then uses feature fusion technology for processing. Finally, three granularities of feature information are introduced into the weight parameters to improve the model, and experimental comparisons are made with a variety of single-channel CNN models in CIFAR10 dataset image classification. The experimental results show that the classification accuracy of the model is significantly improved.
Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors

Due to the breadth of its application domains, Hu-man Activity Recognition (HAR) is a problematic area of human-computer interaction. HAR can be used in remote monitoring of senior healthcare and concern situations in intelligent man-ufacturing, among other applications. HAR based on wearable inertial sensors has been researched identification efficiency in various kinds of human actions considerably more than vision-based HAR. The sensor-based HAR is generally applicable to indoor and outdoor locations without privacy considerations of implementation. In this research, we explore the recognition performance of multiple deep learning (DL) models to recognize everyday living human activities. We developed a deep residual neural network that employed aggregated multi-branch transformation to boost identification performance. The proposed model is called the ResNeXt model. To evaluate its performance, three standard DL models (CNN, LSTM, and CNN-LSTM) are investigated and compared to our proposed model using a standard HAR dataset called Daily Living Activity dataset. These datasets gathered mobility signal data from multimodal sensors (accelerometer, gyroscope, and magnetometer) in three distinct body areas (wrist, hip, and ankle). The experimental findings reveal that the proposed model surpasses other benchmark DL models with maximum accuracy and F1-scores. Furthermore, the findings show that the ResNeXt model is more resistant than other models with fewer training parameters.
Experimental Coverage Performance of a Chaotic Autonomous Mobile Robot

This work, presents the design and the implementation of a mobile robot that can cover given workspaces. The motion commands have been produced by a chaotic system which gives high "randomness" to its behavior. For the implementation of the robot a microcontroller has been used (Arduino Uno). Moreover, the obstacle avoidance has been achieved by using an ultrasonic sensor, which enables the tracking and avoidance of placed obstacles. As a result, the motion trajectory becomes smoother. The robot’s behavior has been studied in scenarios with different starting positions showing promising results.
A Recommender System for Recommending Suitable Products in E-shop Using Explanations

This article proposes a recommender system for recommending relevant products in e-shop using explanations. The proposed system consists of three recommender modules called VIEW, RATING, and PURCHASE. The recommender modules use a content-based filtering approach and a collaborative filtering approach. The proposed recommender system works with explanations that contain arguments why the system recommended the specific product. Based on these explanations the user sees why specific products are recommended by the system. The proposed system was experimentally verified and the results of the experimental verification are discussed.
A Lightweight Approach for Passive Human Localization Using an Infrared Thermal Camera

In this article, we study the problem of passive human localization using an infrared (IR) thermal imaging camera which detects IR radiation emitted by human without carry-on devices and thereby generates a heat map of human body. Rather than directly using the heat map, we propose to exploit temperature of human body and design a lightweight approach for human localization using machine learning techniques. We observe that person-to-camera distance is closely related with the position and the size of a person’s head in the heat map, and several other features, such as variance, skewness, and kurtosis of temperatures in the head region are also good indicators of person-to-camera distance estimation. Accordingly, we propose a set of features and construct a model for inferring person-to-camera distance using machine learning techniques. With the estimated distance, we further compute human localization based on the relative position of the person in the heat map, the estimated person-to-camera distance, and the location and the DFoV of the IR thermal camera. Our experiments in real environments show that the proposed approach can accurately estimate person-to-camera distance and human localization with submeter errors.
Secure Message Handling in Vehicular Energy Networks Using Blockchain and Artificially Intelligent IPFS

In the underlying work, the problems faced during message dissemination in the conventional Vehicular Energy Networks (VENs) like lack of security, breach of personal identities, absence of trust between vehicle owners, etc., are tackled. In this study, a Blockchain (BC) based announcement system is proposed for VENs to ensure secure and reliable announcement dissemination in the proposed network. The proposed system is a three-layered system comprising message dissemination layer, storage layer and BC layer. In the first layer, all the vehicles are registered through a Certificate Authority (CA), which ensures only the legitimate vehicles become part of the proposed network and interact with each other. Later, in the second layer, the data sent by the vehicles is stored at the artificial intelligence based Interplanetary File System (IPFS), which is incorporated with the Road Side Units (RSUs). This ensures reduction in storage cost and data availability. Besides, vehicle owners&#x2019; privacy is ensured by concealing the real identities of the vehicles. Moreover, the hashes of the data stored in the IPFS are stored in BC in the third layer. Also, lightweight trustworthiness verification of the vehicles, reputation based incentivization and concealing predictable trends in vehicles&#x2019; reputation scores are performed in the same layer. Overall, the novelty of the proposed work lies in the fact that the proposed system efficiently tackles different problems encountered in the existing systems simultaneously. Through extensive simulations, it is inferred that the computational time is reduced by 15-18% and the storage overhead is reduced by 80-85%, respectively when storing hash of data on the BC network as compared to storing actual data on the network.
Decoder Design for Massive-MIMO Systems Using Deep Learning

Designing a massive multiple-input multiple-output (M-MIMO) system is complex, time-consuming, and challenging. Conventional MIMO decoders are either inefficient or complex to implement for M-MIMO scenarios. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. In this article, a DL-based iterative network (DLNet) has been proposed for signal decoding in M-MIMO systems. The proposed DLNet decoder is a 40 layer deep neural network (DNN) that can decode M-MIMO signals for uplink Rayleigh and correlated MIMO channels. With the knowledge of the received signals and the M-MIMO channels, the proposed DLNet decoder decodes the messages of all the users. The DLNet considers complex-valued Rayleigh and correlated MIMO channels while decoding. Its training converges faster than other available deep learning (DL) based MIMO decoders. During simulations, the required number of DNN layers, training iterations, and other network parameters are estimated to improve the proposed decoder's performance. In the M-MIMO perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and runtime requirement. Simulation results show that the SER of the proposed DLNet decoder performs better by at least 2 dB than other M-MIMO decoding techniques. The DLNet consumes 800 times less time during decoding than other MIMO decoders for equal transmitting and receiving antenna configurations. At the same time, it has 13 times fewer trainable parameters than the available detection-network algorithm.
Knowledge diffusion–application association during the COVID-19 pandemic: investigating the role of technology intervention

Purpose Knowledge management during the pandemic has been a challenging task due to the sudden intervention of technology in the organisational environment and the unexpected shift to the work-from-home culture. This study aims to investigate the role of technology intervention in the relationship between knowledge diffusion and knowledge application. Design/methodology/approach A cross-sectional study was conducted and data were collected from 541 employees who were working from home during the pandemic in India. Findings This study found significant relationships between knowledge diffusion and technology intervention. This study also observed the mediating role of technology intervention in the relationship between knowledge diffusion and knowledge application. Originality/value Tis study stands with other pioneering studies that have explored the role of technology intervention in the knowledge diffusion–application relationship using the job demand-resource model.
Draw-a-Deep Pattern: Drawing Pattern-Based Smartphone User Authentication Based on Temporal Convolutional Neural Network

Present-day smartphones provide various conveniences, owing to high-end hardware specifications and advanced network technology. Consequently, people rely heavily on smartphones for a myriad of daily-life tasks, such as work scheduling, financial transactions, and social networking, which require a strong and robust user authentication mechanism to protect personal data and privacy. In this study, we propose draw-a-deep-pattern (DDP)—a deep learning-based end-to-end smartphone user authentication method using sequential data obtained from drawing a character or freestyle pattern on the smartphone touchscreen. In our model, a recurrent neural network (RNN) and a temporal convolution neural network (TCN), both of which are specialized in sequential data processing, are employed. The main advantages of the proposed DDP are (1) it is robust to the threats to which current authentication systems are vulnerable, e.g., shoulder surfing attack and smudge attack, and (2) it requires few parameters for training; therefore, the model can be consistently updated in real-time, whenever new training data are available. To verify the performance of the DDP model, we collected data from 40 participants in one of the most unfavorable environments possible, wherein all potential intruders know how the authorized users draw the characters or symbols (shape, direction, stroke, etc.) of the drawing pattern used for authentication. Of the two proposed DDP models, the TCN-based model yielded excellent authentication performance with average values of 0.99%, 1.41%, and 1.23% in terms of AUROC, FAR, and FRR, respectively. Furthermore, this model exhibited improved authentication performance and higher computational efficiency than the RNN-based model in most cases. To contribute to the research/industrial communities, we made our dataset publicly available, thereby allowing anyone studying or developing a behavioral biometric-based user authentication system to use our data without any restrictions.
Real-Time Earthquake Detection and Alerting Behavior of PLUM Ground-Motion-Based Early Warning in the United States

ABSTRACT We examine the real-time earthquake detection and alerting behavior of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm and compare PLUM’s performance with the real-time performance of the current source-characterization-based ShakeAlert System. In the United States (U.S.), PLUM uses a two-station approach to detect earthquakes. Once a detection is confirmed, observed modified Mercalli intensity (MMI) distributions are forecast onto a regular grid, in which the preferred alert regions are grid cells with MMI 4.0+ forecasts. Although locations of dense station coverage allow PLUM to detect small (M &amp;lt; 4.5) earthquakes typically not considered for EEW in the U.S., a PLUM detection on a small earthquake does not always generate an alert. This is because PLUM alerts are determined by current shaking distributions. If the MMI 4.0+ shaking subsides prior to detection confirmation by shaking at a second neighboring station, the prior MMI 4.0+ information will not be in the alert forecasts. Of the 432 M 3.0+ U.S. West Coast earthquakes in 2021, 33 produced ground motions large enough to be detected by PLUM. Twenty-four generated MMI 4.0+ PLUM alerts, whereas ShakeAlert issued public EEW alerts for 13 of these earthquakes. We compare PLUM and ShakeAlert alert regions with ShakeMap and “Did You Feel It?” intensity distributions. Because PLUM alert regions surround stations observed to have strong ground motions (regardless of earthquake magnitude), PLUM alerts reliably include locations that experience significant shaking. This is not necessarily the case for ShakeAlert alert regions when there are large errors in magnitude or epicenter estimates. For two of the largest earthquakes in our real-time dataset, the M 6.0 Antelope Valley and M 5.1 Petrolia earthquakes, the inclusion of PLUM would have improved real-time ShakeAlert performance. Our results indicate that incorporation of PLUM into ShakeAlert will improve the robustness of the EEW system.
Fractal Feature Based Image Resolution Enhancement Using Wavelet–Fractal Transformation in Gradient Domain

The fractal geometries are applied extensively in many applications like pattern recognition, texture analysis and segmentation. The application of fractal geometry requires estimation of the fractal features. The fractal dimension and fractal length are found effective to analyze and measure image features, such as texture, resolution, etc. This paper proposes a new wavelet–fractal technique for image resolution enhancement. The resolution of the wavelet sub-bands are improved using scaling operator and then it is transformed into texture vector. The proposed method then computes fractal dimension and fractal length in gradient domain which is used for resolution enhancement. It is observed that by using scaling operator in the gradient domain, the fractal dimension and fractal length becomes scale invariant. The major advantage of the proposed wavelet–fractal technique is that the feature vector retains fractal dimension and fractal length both. Thus, the resolution enhanced image restores the texture information well. The texture information has also been observed in terms of fractal dimension with varied sample size. We present qualitative and quantitative analysis of the proposed method with existing state of art methods.
Moving objects segmentation using generative adversarial modeling

Moving Objects Segmentation (MOS) is a crucial step in various computer vision applications, such as visual object tracking, autonomous vehicles, human activity analysis, surveillance, and security. Existing MOS approaches suffer from performance degradation due to extreme challenging conditions in real world complex environments such as varying illumination conditions, camouflage objects, dynamic backgrounds, shadows, bad weathers and camera jitters. To address these problems we proposed a novel generative adversarial based framework for moving objects segmentation. Our framework works with one classifier discriminator, one representation learning network and one generator jointly trained to perform MOS in various challenging scenarios. During training the discriminator network acts as a decision maker between real and fake training samples using conditional least squares loss. While the representation learning network provides the difference between the deep features of real and fake training samples using content loss formulation. Another loss term we have exploited to train our generator network is the reconstruction loss that minimizes the difference between the spatial information of real and fake training samples. Moreover, we also propose a novel modified U-net architecture for our generator network showing improved performance over Vanilla U-net model. Experimental evaluations of our proposed method on four benchmark datasets in comparison with thirty-two existing methods has demonstrated the strength of our proposed model.
PDNet: Progressive denoising network via stochastic supervision on reaction-diffusion–advection equation

Recent works on residual network and its variants help us to advance our understanding of deep neural networks as differential equations. Specifically, when taking convolution operator as the combinations of differential operators, deep convolutional neural network (DCNN) can be regarded as partial differential equation (PDE). Learn such PDEs have become popular in image restoration especially for image denoising. While current methods for learning the denoising PDEs face two critical issues. On the one hand, the learned PDEs are not guaranteed to behave as progressive denoising as traditional denoising PDEs. On the other hand, the low level feature may diminish with time increases in PDE. To resolve these issues, this paper designs to learn a specialized PDE network (PDNet) which depicts the information propagation in the network as a reaction-diffusion–advection process. Specifically, a reaction-term is introduced to prevent the low-level features from diminishing in the PDE network, and a decreasing residual gain strategy with stochastic supervision is adopted to facilitate the progressive denoising property. The progressive denoising property not only reveals the semantic of the network path but also regularizes the network training. Compared to vanilla DCNNs, the PDNet has clear physical meaning. Meanwhile PDNet simplifies the ResNet in that batch normalization is removed from the residual blocks and there is only one nonlinear unit in a residual block (2 for ResNet). Extensive experimental results on benchmark datasets verified that PDNet is not only efficient but also effective.
URL Phishing Detection Using Particle Swarm Optimization and燚ata燤ining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process, including neural networks, machine learning, or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique (PSO) to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach, the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers, the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover, it can minimize processing time for identifying the phishing website instead.So, the approach can be beneficial to identify such URLs over the existing contemporary detecting models proposed before.
Mutated Leader Sine-Cosine Algorithm for Secure Smart IoT-Blockchain of Industry 4.0

In modern scenarios, Industry 4.0 entails invention with various advanced technology, and blockchain is one among them.Blockchains are incorporated to enhance privacy, data transparency as well as security for both large and small scale enterprises.Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively.However, because numerous devices and machines are involved, data security and privacy are always concerns.To achieve intelligence in Industry 4.0, blockchain technologies can overcome potential cybersecurity constraints.Nowadays, the blockchain and internet of things (IoT) are gaining more attention because of their favorable outcome in several applications.Though they generate massive data that need to be effectively optimized and in this research work, deep learning-based techniques are employed for this.This paper proposes a novel mutated leader sine cosine algorithm-based deep convolutional neural network (MLSC-DCNN) in order to attain a secure and optimized IoT blockchain for Industry 4.0.Here, an MLSC is hybridized using a mutated leader and sine cosine algorithm to enhance the weight function and minimize the loss factor of DCNN.Finally, the experimentation is carried out for various simulation measures.The comparative analysis is made for Best Tip Selection Method (BTSM), Smart Block-Software Defined Networking (SDN), and the proposed approach.The evaluation results show that the proposed approach attains better performances than BTSM and SDN.