Reviewerly/exHarmony huggingface dataset:
Drone classification from RF fingerprints using deep residual nets

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99% classification accuracy for signal-to-noise ratio (SNR) 0dB and at-10 dB SNR it obtains 5% better classification accuracy compared to the existing framework.
Blockchain Brings Trust to Collaborative Drones and LEO Satellites: An Intelligent Decentralized Learning in the Space

In this paper, we develop a foundation for a constellation of Low Earth Orbit (LEO) satellite IoT by constructing a Blockchain-based framework for continual knowledge sharing and learning collaboratively. This approach is directly applicable for a swarm of Unmanned Aerial Vehicles (UAVs). We ablate Federated Learning (FL) successful features as a basis to ensure high precision of learning inferences at timescales relevant to the underlying time-varying space network and channel dynamics. In such a dynamic setting, there is always a likelihood that miners may be compromised or fail to propagate information in time because of some intrinsic factors such as channel impairments, satellite handovers and attacks. Such transmission failures often lead to undesirable forking events in the Blockchain. Consequently, maintaining a low energy consumption and smallish delay in such an erratic network is highly nontrivial and challenging. To quantify the impacts of the forking and minimize the occurrence of such unwanted events and their adverse effects, we develop a procedure to estimate the expected energy consumption for a given set of miners, block transmissions, and LEOs’ or UAVs’ mobility. Besides, we shed light on deep learning-based resource allocation for mobile mining and demonstrate the synergic gain of FL with Blockchain.
Deep video action clustering via spatio-temporal feature learning

The recent years have witnessed significant advances in deep video action recognition. However, the performances of deep learning-based video action recognition methods are limited in the case of relatively small samples or no labeled samples. Therefore, trying to use unlabeled video data to generate clustering label is essential for small sample learning and zero sample learning. In this paper, we propose a novel deep video action clustering network, which aims to learn the similarity relationship among the unlabeled video samples, and generate the clustering label for each video sample. Specifically, the proposed method simultaneously learns the spatio-temporal features and subspace representations under a jointly optimized framework. It consists of a 3D U-Net self-representation generator, a video-clip reconstruction discriminator, and a confidence-based feedback mechanism. The 3D U-Net self-representation generator learns the spatio-temporal features of the video clips and produces subspace representation matrix. Then, the similarity graph is constructed based on this subspace representation matrix, and the clustering result is obtained. In the learning procedure, a confidence-based feedback mechanism is designed to feed the high-confidence labels of partial samples back to further guide the subspace structure learning, so that the optimal result can be obtained. During training, the video-clip reconstruction discriminator is introduced to evaluate the reconstructed video clips, which is beneficial for capturing the discriminative spatio-temporal features. Experimental results on a video benchmark dataset demonstrate the effectiveness of the proposed method.
Path planning for the autonomous robots using modified grey wolf optimization approach

Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.
“Digital Twins” in Education: Prospects and Reality

This article is aimed at understanding the revolutionary in meaning and “digital” in essence metamorphoses in education and the prospects for the emergence of a new educational network reality based on them. The focus of the discussion is not so much on digital innovations and transformations in education as such, but rather on the need to understand the possible prospects and results of these social network changes and modifications brought to life by the next stage of the digital revolution in education. It is proposed to include a wide range of network technologies and methodologies among the latest scientific and technological innovations taking into account the fact that the main focus of the work is on understanding the process / result of implementing the methodology of “digital twins” in education. Despite the fact that the latter is only at the beginning of its implementation in a broad educational context, the paper raises the question of the social consequences of implementing the methodology for constructing the latest network reality of “digital twins in education”, which has all the chances in the future to become a unique network of networks of “digital twins” of various social actors in education. The article discusses the managerial potential of developing and the difficulties of implementing the digital twin methodology, the expected effects of implementing the latter in education, and the socio-technological consequences of converting this technology into a new network educational reality in the Russian Federation.
Trace metal elements in phosphate rock wastes from the Djebel Onk mining area (Tébessa, eastern Algeria): A geochemical study and environmental implications

Algeria is one of the phosphate-rich countries where many deposits are found in the Tébessa region which are currently being mined from the Kef Essenoun quarries. The processing of run-off-mine, to increase P2O5 grads in the commercial product, includes the use of wet and dry procedures which in turn leads to the production of large amounts of wastes. The present study focuses on tracking the amount of trace metal elements (TMe) in the phosphate rock wastes resulting from the processing of phosphate run-of-mine. The representative samples of solid rejects and sludge wastes from both processing ways were collected and analysed using SEM and FTIR to characterise the materials, whereas ICP-AES and ICP-MS were used to determine major and TMe compositions, respectively. The results showed that the analysed wastes are P-enriched (P2O5 = 20 wt% - 27 wt %) together with similar mineralogy such as natural phosphorites (carbonate fluor-apatite), but they displayed an enhancement in matrix phases. Moreover, the overall tendency showed that the concentrations of TMe are relatively increased with a decrease in grain size diameter where higher contents of V, Cr, Co, Ni, Cu, Zn, As, Zr, Nb and Cd have been recorded in sludge and fine fraction from the wet way process (Rw −0.08 mm) when compared to other samples. The fine fractions generally contain the matrix that enhances almost all of TMe except for Sr and U which are hosted in apatite. Risk assessment of TMe showed that both raw phosphorites and their wastes, as all worldwide sedimentary phosphorites, yield some hazardous TMe exceeding the standards for soils (e.g., U, Cd, Cr, Mo, V and Tl). Hence, owing to the waste exposure and mobilisation of the finest particles by wind and rainfall, these hazardous metals could have harmful effects on the environment as well as human health.
Tidying Deep Saliency Prediction Architectures

Learning computational models for visual attention (saliency estimation) is an effort to inch machines/robots closer to human visual cognitive abilities. Data-driven efforts have dominated the landscape since the introduction of deep neural network architectures. In deep learning research, the choices in architecture design are often empirical and frequently lead to more complex models than necessary. The complexity, in turn, hinders the application requirements. In this paper, we identify four key components of saliency models, i.e., input features, multi-level integration, readout architecture, and loss functions. We review the existing state of the art models on these four components and propose novel and simpler alternatives. As a result, we propose two novel end-to-end architectures called SimpleNet and MDNSal, which are neater, minimal, more interpretable and achieve state of the art performance on public saliency benchmarks. SimpleNet is an optimized encoder-decoder architecture and brings notable performance gains on the SALICON dataset (the largest saliency benchmark). MDNSal is a parametric model that directly predicts parameters of a GMM distribution and is aimed to bring more interpretability to the prediction maps. The proposed saliency models can be inferred at 25fps, making them suitable for real-time applications. Code and pre-trained models are available at https://github.com/samyak0210/saliency.
Cyber-threat perception and risk management in the Swedish financial sector

The financial sector relies heavily on information systems for business. This study sets out to investigate cyber situation awareness in the financial sector in Sweden, by examining what information elements that are needed for a common operational picture, and exploring how key actors perceive cyber-threats. Data was collected through a survey and a series of interviews with key actors in the sector in conjunction with a national level crisis management exercise. The data was then analyzed and contrasted to theory. Conclusions were drawn and results discussed. Finally, possible mitigation actions were suggested. It was found that actors in the Swedish financial sector have a well developed crisis management working concept. However, information about rational adversaries that cause prolonged disturbances is possibly not collected, analyzed and utilized systematically. Much effort is put into ensuring that timely and relevant information from organizations is shared in an efficient manner. The sector perceives cyber-threats against the underlying financial infrastructure, as well as against IT service availability and data confidentiality, besides financial theft. The sector has particular concerns for the potential of reputational loss due to cyberattacks. There are also special concerns about the insider threat. Respondents agree that risk management has to account for cyber risk. A possible route to enhance risk management practices is to ensure that cyber personnel is integrated in crisis management teams.
A movie recommendation method based on users' positive and negative profiles

In the traditional content-based recommendation method, we usually use the movies users watched before or rated to represent their profile. However, there are many movies that users have never seen or rated. For an unrated movie, there are two possibilities: maybe the user likes it or does not like it. In this paper, we first focus on how to identify users' preferences for movies by using a collaborative filtering algorithm to predict the users’ movie ratings. We can then create two movie lists for each user, where one is the movies the user likes (with higher predicting or true ratings), and the other is the movies the user does not like (with lower predicting or true ratings). Based on these two movie lists, we establish a user positive profile and a user negative profile. Therefore, our algorithm will recommend to users movies that are most similar to their positive profile and most different from their negative profile. Finally, our experiments show that our method can improve the MAE index of the traditional collaborative filtering method by 12.54%, the MAPE index by 17.68%, and the F1 index by 10.16%.
Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups

Automated gender classification has important applications in many domains, such as demographic research, law enforcement, online advertising, as well as human-computer interaction. Recent research has questioned the fairness of this technology across gender and race. Specifically, the majority of the studies raised the concern of higher error rates of the face-based gender classification system for darker-skinned people like African-American and for women. However, to date, the majority of existing studies were limited to African-American and Caucasian only. The aim of this paper is to investigate the differential performance of the gender classification algorithms across gender-race groups. To this aim, we investigate the impact of (a) architectural differences in the deep learning algorithms and (b) training set imbalance, as a potential source of bias causing differential performance across gender and race. Experimental investigations are conducted on two latest large-scale publicly available facial attribute datasets, namely, UTKFace and FairFace. The experimental results suggested that the algorithms with architectural differences varied in performance with consistency towards specific gender-race groups. For instance, for all the algorithms used, Black females (Black race in general) always obtained the least accuracy rates. Middle Eastern males and Latino females obtained higher accuracy rates most of the time. Training set imbalance further widens the gap in the unequal accuracy rates across all gender-race groups. Further investigations using facial landmarks suggested that facial morphological differences due to the bone structure influenced by genetic and environmental factors could be the cause of the least performance of Black females and Black race, in general.
Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect li... | Find, read and cite all the research you need on Tech Science Press
Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block

Object detection in optical remote sensing images (ORSIs) remains a difficult task because ORSIs always have some specific characteristics such as scale-differences between classes, numerous instances in one image and complex background texture. To address these problems, we propose a new Multi-Feature Pyramid Network (MFPNet) with Receptive Field Block (RFB) that integrates both local and global features to detect scattered objects and targets with scale-differences in ORSIs. We build a Multi-Feature Pyramid Module (M-FPM) with two cascaded convolution pyramids as the main structure of MFPNet, which handles object detection of different scales very well. RFB is designed to construct local context information, which makes the network more suitable for the objects detection around complex background. Asymmetric convolution kernel is introduced to RFB to improve the ability of feature attraction by adding nonlinear transformation. Then, a two-step detection network is constructed to combine the M-FPM and RFB to obtain more accurate results. Through a comprehensive evaluation of the experimental results on two publicly available remote sensing datasets Levir and DIOR, we demonstrate that our method outperforms state-of-the-art networks for about 1.3% mAP in Levir dataset and 4.1% mAP in DIOR dataset. Experimental results prove the effectiveness of our method in ORSIs of complex environments.
Incidence of Gender in the Digital Competence of Higher Education Teachers in Research Work: Analysis with Descriptive and Comparative Methods

The purpose of this study is to analyse and compare the level of digital competence of higher education teaching staff in research work through the use of ICT resources. For this purpose, an ex post facto design was employed together with an instrument composed of 30 items classified into seven dimensions: digital skills, digital ethics, digital flow, anxiety towards ICT, quality of ICT resources, intention to use ICT, and ICT integration. The sample consisted of a total of 1704 higher education teachers from all over Spain. The results showed that, overall, there were no significant differences in the level of digital competence of teaching staff between the two genders. However, significant differences were found in the following dimensions: digital skills, digital ethics, ICT anxiety, quality of ICT resources, and intention to use ICT. These findings highlight the urgent need for university institutions to propose training plans to improve the digital competences of their teaching and research staff in those dimensions that have been found to be deficient.
A color image encryption algorithm based on an improved Hénon map

Abstract Chaos is widely used in secure communication and cryptography due to its randomness, unpredictability, non-periodicity and high sensitivity to initial values and parameters. However, there are many risks in some existing chaotic image encryption algorithms because they use the the chaotic map without complex dynamic characteristics. To overcome these weaknesses, in this paper introduced an improved Hénon map, and the dynamic analysis results show that the improved Hénon map has more rich chaotic behaviors and better complexity. In addition, we designed a color image encryption scheme using the improved Hénon sequences. For this encryption algorithm, color image is divided into R, G, B primary colors, then R, G, B primary colors are scrambled and diffused by the improved Hénon sequences. The simulation results illustrate that color image encryption algorithm security is advanced by usingimproved Hénon map.
Brain MRI super-resolution using coupled-projection residual network

Magnetic Resonance Imaging (MRI) has been widely used in clinical application and pathology research to help doctors provide better diagnoses. However, accurate diagnosis by MRI remains a great challenge, as images obtained via current MRI techniques usually have low resolutions. Improving MRI image quality and resolution has thus become a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. CPRN consists of two complementary sub-networks: a shallow network and a deep one, which maintain content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection to better retain the MR image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MR images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MR images. For effective feature fusion between the deep and shallow sub-networks, a step-wise connection (CPRN_S) is designed, inspired by the human cognitive process (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance compared with the state-of-the-art.
Outlier detection based on weighted neighbourhood information network for mixed-valued datasets

Outlier detection is of great importance in industry as unexpected errors or faults, abnormal behaviours or phenomena, etc. can occur due to a variety of human, system, and environmental reasons. To identify and analyse these rare items, events or observations can find either anomalies or novelties and, as a result, can help avoid potential unexpected consequences or improve industrial system performance. The operating data collected from industrial systems in the Industry 4.0 era are characterized as multi-attribute (e.g., both numerical and categorical) compared to previous studies. Therefore, a new outlier detection method for mixed-valued datasets based on the weighted network model is proposed in this paper. Concretely, a weighted neighbourhood information network (WNIN) is constructed by considering the neighbourhood relations and similarities among objects to represent a dataset with mixed-valued attributes (DMA). A tailored Markov random walk method is employed to detect outlier on the predefined network model. After reaching the equilibrium, the inlier score is defined according to the out-degree of nodes in the WNIN to represent the inlier degree of objects. Experiments on two real datasets and a case study illustrate the effectiveness and adaptability of the proposed method.