Reviewerly/exHarmony huggingface dataset:
Distributed deep learning-based signal classification for time–frequency synchronization in wireless networks

In this paper, we propose a novel distributed deep learning (DL) network for signal classification to achieve accurate time–frequency synchronization in wireless communication networks. Restricted by the non-random signal generated by the transmitter, the existing time–frequency synchronization algorithms cannot obtain reliable synchronization results. With the development of DL, feature-based signal classification has become an effective way to improve the precision of time–frequency synchronization. Considering the multipath effect in the practical propagation environments, a distributed DL network for wireless communication is proposed, where the received signal is classified by the distributed DL network. Specifically, preprocessed images are obtained by applying the discrete Fourier transform (DFT) and short-time Fourier transform (STFT) to facilitate the signal feature extraction. Convolutional neural network (CNN) architecture is designed for feature extraction on the preprocessed images. Meanwhile, a feature fusion module based on attention mechanisms is proposed to fuse data adaptively and obtain the final signal classification results. Simulation results show that the proposed distributed DL network can achieve higher classification accuracy than traditional DL networks under multiple modulation schemes. More importantly, reliable signal classification can effectively improve the performance of time–frequency synchronization in wireless communication networks.
An adaptive image enhancement approach for safety monitoring robot under insufficient illumination condition

The safety monitoring robots have been a potential solution to the task of patrol and inspection under uncontrolled complex environments such as tunnels. However, unclear semantic information and noises in low-light images will cause disturbance to the robot vision system. The deep learning-based enhancement is proven to advance the vision under poor illuminating conditions. However, the difficulties of excessive manual cost in collecting image pairs and image noise amplification are still challenging. In this work, we introduce a new adaptive image enhancement pipeline, which enables robots to self-adaptive to complex illumination conditions. With the pipeline, the IE-Module is proposed to learn image enhancement information from multi-scale feature blocks, and the SEnhance Module is used to eliminate the damage of denoising to normal pixels of the image. To make the algorithm better coupled with the safety monitoring robot, the algorithm is further enhanced with a filter threshold for adaptive to varied illumination conditions. The effectiveness of our method on both standard datasets and real-world scenarios was verified with experiments and real applications. The experiments on a low-light image benchmark as well as SLAM and 6D pose estimation experiments demonstrated that our method is superior to state-of-the-art methods qualitatively and quantitatively. Moreover, the proposed algorithm has been deployed in a real safety monitoring robot, which successfully demonstrates its capacity to perform patrol and inspection tasks in a low-light environment.
BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Over the past era, subgraph mining from a large collection of a graph database is a crucial problem. our proposed work introduces a Blockchain-based Triune Layered Architecture for authenticated subgraph query search in large-scale dynamic graphs. The two-fold process is handled in BTLA-LSDG: graph indexing and authenticated query search (query processing), which are implemented in triune layers (Data Generation Layer, Data Storage Layer, and Service Layer). Initially, data owners are authenticated to the blockchain using the Four-Q-Curve algorithm. The graph index is constructed by data owners and the merged graph index is constructed by service providers. Based on the uploaded graph index, the hash index is constructed using SHA-3. On the other hand, the data user submits a query with authentication. For every authenticated query, the four-fold process is handled. Firstly, Multi-Constraint-based Belief Entropy function is used for feature set computations for a given query. Then dual similarity-based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Thirdly, a Recurrent Neural Network (RNN) is used for subgraph isomorphic testing. Finally, the graph index refinement process is undertaken to improve the query results. This experiment is implemented using a Hadoop environment and the results show better efficiency in terms of Scalability, Security and Storage. Furthermore, it is tested with Precision, Recall, F-measure, Accuracy, Error Rate, Query Response Time and Positive Results.
Authenticating GNSS civilian signals: a survey

Abstract Civilian services of Global Navigation Satellite System are threatened by spoofing attacks since it is hard to determine the authenticity of a navigation signal with a detailed structure open to the public. Signal authentication effectively protects the security of the signal by attaching unforgeable information to one or several elements of the signal. Receivers can verify the authenticity of the signal by extracting and validating this information. Developing good signal authentication schemes requires understanding possible spoofing modes, signal element specialty, and performance evaluation methods. This paper is an overview of navigation signal authentication, where the theories and reported approaches are described in detail. A design/performance matrix that demonstrates the advantages and defects of the signal element and its authentication design is summarized. Recommendations are proposed to improve the robustness, security, efficiency, and implementation hardness for future designs of navigation signal authentication.
Central difference information filtering phase unwrapping algorithm based on deep learning

This paper presents a deep learning-based central difference information filtering phase unwrapping algorithm. First, the central difference information filter (CDIF) is introduced into phase unwrapping for wrapped phase images for the first time to our knowledge, and is combined with an efficient local phase gradient estimation technique, and an efficient path-following strategy based on heap sorting to construct a CDIF phase unwrapping procedure. Second, a region segmentation model based on deep learning is constructed to divide the wrapped phase images into a series of segmented regions along the edge of interferometric fringes, which is beneficial to improve the accuracy and efficiency of subsequent phase unwrapping. Finally, each segmented region of the wrapped phase images is unwrapped separately using the CDIF phase unwrapping procedure, and the unwrapping results of each segmented region are combined according to the phase consistency criterion of adjacent regions of the wrapped phase images to obtain the unwrapped phase of the whole wrapped phase images. Results delivered by the experiments on synthetic data and experimental data demonstrate the robustness and acceptable efficiency of the proposed method in phase unwrapping for the wrapped phase images, compared with some commonly used algorithms.
Wavelength-shifting interferometry using the frequency-modulated Chirp-Z transform and effective multi-harmonic sampling

In wavelength-shifting measurements of the transparent flat, multi-surface harmonics of the measured surfaces with different frequencies provide the corresponding intensity information in the captured interferograms. The limitations of such measurements arise from the limited measurement distance and unsatisfactory frequency detection accuracy. Therefore, a local spectral intensifying method using the Chirp-Z transform is introduced. Based on the distribution characteristics of the phase-shifts within each harmonic period, the calculation method of the measurement distance and the available sampling frequency that can avoid under-sampling is proposed. Based on the above methods, efficient multi-surface phase demodulation can be implemented. Using the Zernike polynomials, the robustness of the proposed algorithm to some common measurement errors is analyzed and demonstrated. In experiments, two transparent plates with thicknesses of 6.35 mm and 15 mm were measured using a wavelength-shifting interferometer. The RMS errors of three repeatability measurements in the experiments are 5.71 nm and 0.64 nm, respectively. Both the simulation analysis and the experiments verify the effectiveness of the proposed method.
Sparse Adversarial Unsupervised Domain Adaptation With Deep Dictionary Learning for Traffic Scene Classification

In recent years, the accurate recognition of traffic scenes has played a key role in autonomous vehicle operations. However, most works in this area do not address the domain shift issue where the classification performance is degraded when the distribution of the source and target images are different due to weather changes. Also, lack of sparsity of current studies results in sample complexity and overfitting issues. To mitigate these challenges, this paper proposes a novel Sparse Adversarial Domain Adaptation (SADA) model for traffic scene classification. Our objective is to learn a scene classifier on a source domain with sunny weather and transfer its knowledge to a target domain with different weather (i.e., cloudy, rainy, and snowy) to enhance the classification performance on the target. First, a sparse representation is learned from the source traffic scenes via nonlinear dictionary learning in the latent space of a deep classifier. Then, a conditional generative adversarial network is devised to capture the distribution of the source sparse codes. Finally, a domain invariant sparse feature extractor is developed via a minimax game to align the sparse codes of the target domain images with the source; hence, providing domain adaptation for traffic scene classification using a deep neural network. Experimental results on a real-world dataset collected by the Honda Research Institute (HRI) indicate the superior performance of the proposed SADA compared to current traffic scene classification methods and state-of-the-art domain adaptation frameworks.
Multi-passage extraction-based machine reading comprehension based on verification sorting

For traditional single-passage machine reading comprehension, the text data of a single passage does not well reflect the complexity of practical application scenarios. Many researchers have shifted their research goals to study multi-passage machine reading comprehension. To solve the problem of multi-passage machine reading comprehension, this paper proposes a unified multi-module end-to-end reading comprehension model for passage retrieval, answer extraction, and multi-answer verification ranking using a pre-trained model. In this paper, the passage retrieval module selects the passage fragments with the highest probability of survival, and the answer extraction component extracts the possible candidate answers in each passage. The multi-answer verification ranking component uses an attention mechanism to fuse multiple candidate answer feature representations, obtains the score of each candidate answer and selects the candidate answer with the highest score as the final answer. Finally, through experimental validation, the proposed model achieves scores of 49.59 and 46.28 on the evaluation metrics BLUE-4 and ROUGH-L on the DuReader dataset, and scores of 44.78 and 46.45 on the metrics BLUE-1 and ROUGH-L on the MS-MARCO dataset, respectively.
On the properties of residential rooftop azimuth and tilt uncertainties for photovoltaic power generation modeling and hosting capacity analysis

One of the essential epistemic uncertainties that has not yet been studied enough for distributed photovoltaic systems is the azimuth and tilt of rooftop photovoltaic panels, as previous studies of grid impacts and hosting capacity have tended to assume uniform and optimal roof facet conditions. In this study, rooftop facet orientation distributions are presented and analyzed for all single-family buildings in the Swedish city of Uppsala, based on LiDAR-based data that consist of every roof facet from the around 13,500 single-family buildings in the city. From these distributions, novel methods to proportionally include less suitable roofs for every penetration level are proposed using a simple method based on normal and uniform probability density functions, and are tested for both time-series and stochastic hosting capacity analysis. The results show that under the assumption that the best roof facets are utilized first, a uniform distribution for rooftop facet azimuth and a normal distribution for rooftop facet tilt with parameters that depend linearly on the penetration level were shown to be accurate. The hosting capacity simulations demonstrate how the proposed methods perform significantly better in estimating the photovoltaic hosting capacity than the more common simplified methods for both time-series and stochastic hosting capacity analysis. The proposed model could help distribution system operators as well as researchers in this area to model the rooftop facet orientation uncertainty better and improve the quality of aggregated photovoltaic generation models and hosting capacity analyses.
Breaking the routine: spatial hypertext concepts for active decision making in recommender systems

Recommender Systems are omnipresent in our digital life. Most notably, various media platforms guide us in selecting videos, but recommender systems are also used for more serious goals, such as news selection, political orientation and work decisions. As argued in this survey and position article, the paradigm of recommendation-based feeds has changed user behaviour from active decision making to rather passively following recommendations and accepting possibly suboptimal choices that are deemed “good enough”. We provide a historic overview of media selection, discuss assumptions and goals of recommender systems and identify their shortcomings, based on existing literature. Then, the perspective changes to hypertext as a paradigm for structuring information and active decision making. To illustrate the relevance and importance of active decision making, we present a use case in the field of TV or media selection and (as a proof of concept) carried over to another application domain: maintenance in industry. In the discussion section, we focus on categorising these actions on a spectrum of “system-1” (fast and automated) tasks and “system-2” (critical thinking) tasks. Further, we argue how users can profit from tools that combine active (spatial) structuring and categorising with automatic recommendations, for professional tasks as well as private, leisure activities.
Text mining techniques for identifying failure modes

Purpose The purpose of this paper is to compare the effectiveness of the various text mining techniques that can be used to classify maintenance work-order records into their respective failure modes, focussing on the choice of algorithm and preprocessing transforms. Three algorithms are evaluated, namely Bernoulli Naïve Bayes, multinomial Naïve Bayes and support vector machines. Design/methodology/approach The paper has both a theoretical and experimental component. In the literature review, the various algorithms and preprocessing techniques used in text classification is considered from three perspectives: the domain-specific maintenance literature, the broader short-form literature and the general text classification literature. The experimental component consists of a 5 × 2 nested cross-validation with an inner optimisation loop performed using a randomised search procedure. Findings From the literature review, the aspects most affected by short document length are identified as the feature representation scheme, higher-order n-grams, document length normalisation, stemming, stop-word removal and algorithm selection. However, from the experimental analysis, the selection of preprocessing transforms seemed more dependent on the particular algorithm than on short document length. Multinomial Naïve Bayes performs marginally better than the other algorithms, but overall, the performances of the optimised models are comparable. Originality/value This work highlights the importance of model optimisation, including the selection of preprocessing transforms. Not only did the optimisation improve the performance of all the algorithms substantially, but it also affects model comparisons, with multinomial Naïve Bayes going from the worst to the best performing algorithm.
An adaptive and interactive learning toolkit (iLearn)

“iLearn” is a mobile learning application built to promote online teaching and learning for primary education. iLearn provides competency-based adaptivity with a recommender algorithm, learning style-based adaptivity, and interactive learning for primary students. It provides a customizable interactive video lesson authoring tool for teachers. This application is designed to support low resource and power utilization. Therefore, the underlying architecture is built to support these major features with a detailed design of the anatomy of the application’s internal functionality. The application is built with a user-friendly interface which received positive feedback in the system usability survey conducted
A note on the complex and bicomplex valued neural networks

In this paper we first write a proof of the perceptron convergence algorithm for the complex multivalued neural networks (CMVNNs). Our primary goal is to formulate and prove the perceptron convergence algorithm for the bicomplex multivalued neural networks (BMVNNs) and other important results in the theory of neural networks based on a bicomplex algebra.
Characterization and Detection of Cross-Router Covert Channels

In covert channel attacks, an adversary seeks various means to influence a tangible characteristic of a system, and then makes the systems leak information by measuring this characteristic. Covert channels are, by nature, very elusive. This makes it very difficult to identify them and defend against attacks that use these channels to leak sensitive information. Thus, they are a serious threat to the security of many systems. In this paper, we present two network timing covert channel attacks, and a defense mechanism against them. The purpose of the proposed attacks is to leak sensitive information between two logically separated (or isolated) networks that are hosted by a single router – one that is connected to the Internet, and another that is isolated and contains sensitive information. The attacks build on the fact that the response time of the router for a specific type of packet sent from a device that is connected to it is usually predictable, given the network topology. By interacting with the single shared router in a specific manner, an attacker can increase the router’s packet response time. The interaction is determined based on the information to be leaked, so the receiver (a computer located on the Internet-connected network) can measure the delayed packet response times and decode the sender’s signals (which operates from the isolated network) to receive classified information. The two classes of attacks presented in this work differ in the way that the delay is caused. In the cross-router covert channel (CRCC) attack, the sender overloads the router with control-plane packets; in the Wi-Fi micro-jamming attack, the sender uses a pre-installed implant to transmit single-tone signals in the 2.4GHz frequency range, disturbing the router’s packet transmissions. We showed that both attacks can influence a wide range of router brands and Wi-Fi capable devices and evaluated the optimal settings for both attacks when using different types of packets, transmission power, and data transmission rates. Our proposed defense mechanism is based on semi-supervised machine learning and deep learning algorithms, which are both used for novelty detection in network traffic. By detecting unusual traffic, we can identify the disturbances needed to leak the information. The system can then respond by blocking the suspicious devices that are involved in the attack. We evaluated the attacks in noisy and noise-free environments, successfully detecting both attacks in both environments. All the data and code, which includes the implementation of the attacks and the defense mechanism, is published as a benefit to the research community.
Research Competence Development in Higher Education Through a Virtual Educational Escape Room

The development of research competence in teacher training is essential to improve educational quality. For this reason, it is necessary that educational processes proposed at university adapt to current demands, incorporating gamification procedures and new technologies as teaching-learning tools. The objective of this chapter was to analyze the application of an educational escape room for the development of research competence in students of the Primary Education Degree. The results of this study showed that the application of the virtual escape room allowed the development of positive attitudes towards research, as well as the strengthening of some of skills and concepts acquired. In the same way, the importance of the university professor as a guide and counselor of educational processes planned in the Primary Education Degree was highlighted. The use of the virtual escape room emerges as a mechanism for the research competence development in higher education.
Google Coral-based edge computing person reidentification using human parsing combined with analytical method

Person reidentification (re-ID) is becoming one of the most significant application areas of computer vision due to its importance for science and social security. Due to enormous size and scale of camera systems it is beneficial to develop edge computing re-ID applications where at least part of the analysis could be performed by the cameras. However, conventional re-ID relies heavily on deep learning (DL) computationally demanding models which are not readily applicable for edge computing. In this paper we adapt a recently proposed re-ID method that combines DL human parsing with analytical feature extraction and ranking schemes to be more suitable for edge computing re-ID. First, we compare parsers that use ResNet101, ResNet18, MobileNetV2, and OSNet backbones and show that parsing can be performed using compact backbones with sufficient accuracy. Second, we transfer parsers to tensor processing unit (TPU) of Google Coral Dev Board and show that it can act as a portable edge computing re-ID station. We also implement the analytical part of re-ID method on Coral CPU to ensure that it can perform a complete re-ID cycle. For quantitative analysis we compare inference speed, parsing masks, and re-ID accuracy on GPU and Coral TPU depending on parser backbone. We also discuss possible application scenarios of edge computing in re-ID taking into account known limitations mainly related to memory and storage space of portable devices.