Reviewerly/exHarmony huggingface dataset:
Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k-means, fuzzy c-means, and hierarchical clustering algorithms). The proposed models were used to predict both tilt and orientation angles by two different data sets (month, day, and time variables data set) and (month, day, time, Isc, Voc, and power radiation variables data sets). The results revealed that the proposed MLP/CMLP-LR systems are able to increase the prediction rate and decrease the mean square error rate as compared to conventional models in both single and dual axis solar tracking systems. The new developed intelligent systems achieved less number of overall connections, less number of neurons, and less time complexity. The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real-world applications (i.e., solar heating systems, solar lightening systems, factories, and solar powered ventilation.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping

We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the motion field by minimizing the mutual information between segments. Then, it uses the segments to learn object models that can be used for detection in a static image. Static and dynamic models are represented by deep neural networks trained jointly in a bootstrapping strategy, which enables extrapolation to previously unseen objects. While the training process requires motion, the resulting object segmentation network can be used on either static images or videos at inference time. As the volume of seen videos grows, more and more objects are seen moving, priming their detection, which then serves as a regularizer for new objects, turning our method into unsupervised continual learning to segment objects. Our models are compared to the state of the art in both video object segmentation and salient object detection. In the six benchmark datasets tested, our models compare favorably even to those using pixel-level supervision, despite requiring no manual annotation.
Blind Doppler Tracking and Beacon Detection for Opportunistic Navigation with LEO Satellite Signals

A Doppler tracking and beacon detection framework for blind opportunistic navigation (BON) with low Earth orbit (LEO) satellite signals is proposed. The BON framework cognitively deciphers partially known signals of opportunity (SOPs) and exploits them for navigation purposes. When only the bandwidth and length of an Mary phase shift keying (MPSK) SOP beacon is available, the BON framework enables acquisition and tracking of terrestrial and space-based SOPs in a blind fashion. A computationally efficient algorithm is presented to blindly detect the beacon signals and estimate the Doppler frequency. The BON framework is applied to decipher the C/A pseudorandom noise (PRN) sequences from four GPS satellites. The experimental results show that the BON framework is capable of cognitively detecting the PRNs of GPS satellites with a percentage of correctly detected chips ranging between 91 % and 99 %. Another experimental example with signals from two Orbcomm LEO satellites is presented, demonstrating an unmanned aerial vehicle (UAV) navigating via the BON framework. The UAV traversed a total trajectory of 782 m, achieving a position root mean-squared error (RMSE) of 21.2 m.
TPDE: A tri-population differential evolution based on zonal-constraint stepped division mechanism and multiple adaptive guided mutation strategies

Differential evolution (DE) has been recognized as one of the most effective algorithms for solving numerical optimization problems. In this paper, we propose a tri-population differential evolution (TPDE) to further enhance the search capability of DE. More specifically, the parent population is partitioned into three sub-populations with different emphasises at each iteration based on a newly proposed zonal-constraint stepped division (ZSD) mechanism, which determines the size of each sub-population according to not only individual's fitness value but also the evolutionary process. To make the best of information provided by elite individuals and play their leading role on other individuals, three elite-guided mutation strategies are presented for each sub-population. Moreover, three sets of adaptive control parameters including the scale factor F and crossover rate CR are configured for three mutations according to Gaussian distribution model, Cauchy distribution model and a triangular wave function respectively. The design of mutation strategies and control parameters for each sub-population is based on the principle of balancing the global exploration and local exploitation capabilities. To evaluate the performance of TPDE, comparative experiments are conducted based on 59 benchmark functions from CEC2014 and CEC2017 test suites. The results indicate that the proposed TPDE is significantly better than, or at least comparable to the recent nine state-of-the-art DE variants.
Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding

Image processing is one of the major research regions in the most recent decades, in which, image segmentation has gained more popularity due to its enormous applications. Before segmenting an image, it has to be pre-processed using thresholding techniques. A properly thresholded image would give better segmentation results. Many researchers have contributed significant algorithms based on Otsu and Kapur based methods and reported outstanding results. These methods are suitable for bi-level as well as multilevel thresholding cases, however, the process of selecting the optimal thresholds in the case of multilevel thresholding is difficult and time consuming. To avoid this problem, this paper investigates the ability of two different algorithms namely: BOA (Butterfly Optimization Algorithm) and GBMO (Gases Brownian Motion Optimization) to determine the optimal threshold levels for image segmentation. The BOA is inspired from natural behaviour of butterflies which have special style of information propagation based on intensity of fragrance; whereas, GBMO is based on law of motion of gases which have greater ability to trade-off between exploration and exploitation. The candidate solutions were created using image histogram based on selected algorithms, and then they were updated according to the characteristics of each algorithm. The solutions are estimated using Otsu’s between class variance as fitness function during the process of optimization. The performance of the proposed algorithms has been evaluated using various benchmark images and has been compared with seven different algorithms such as MFO (Moth Flame Optimization), WOA (Whale Optimization Algorithm), SSO (Social Spider Optimization), FASSO (Firefly Assisted Social Spider Optimization), FA (Firefly Algorithm), SCA (Sine Cosine Algorithm), and HS (Harmony Search). The outcomes have been analysed based on various metrics like fitness value, PSNR and SSIM, as well as time complexity and the Wilcoxon statistical test. The experimental results showed that the proposed methods outperformed the other algorithms. Among BOA and GBMO, BOA yields better results than GBMO for all images at all threshold levels.
Exploring students’ learning experience in online education: analysis and improvement proposals based on the case of a Spanish open learning university

Not surprisingly, the number of online universities continues to expand-especially in Covid-19 times. These institutions all offer "online education" with diverse institutional, technological, and pedagogical processes. However, a fundamental element has to do with the experience of the students, and how they adapt to the educational model of the online university in which they are studying. In this article, we present the main results of the case-study developed in one of the most historical and relevant virtual universities in an international context. We have explored and analysed the process of adaptation to the educational model by the student body, and their perceptions of their interactions with the pedagogical, institutional, and technological elements designed to support their learning. Qualitative and quantitative methods are used to gather and analyse the data. From 1715 students who participated in the survey and the perceptions of 30 students individually interviewed, the results show positive evaluations regarding the integration and adoption of technological competencies, and also, that the online education generally serves as a responsive model to the emergent needs of the learner. However, the results also show that students have important concerns regarding the pedagogical and institutional support provided.
Deep-Learning-Aided RF Fingerprinting for NFC Security

Given the ever increasing use of near field communication (NFC), the security of this system is becoming increasingly important. Recently, radio frequency (RF) fingerprinting, where the physical RF characteristics of a communication device are used as a means to provide guarantees of authenticity and security, has received serious consideration due to the uniqueness of these characteristics, making cloning difficult. In this article, we discuss the feasibility of RF fingerprinting assisted by deep learning for use in identifying NFC tags. To this end, we implement a hardware testbed with an off-the-shelf NFC reader and software defined radio. An RF signal corresponding to one-bit transmission from the NFC tag is used to extract the RF characteristics, which enables rapid identification. Three different types of deep neural network are used, namely fully connected layer-based neural network” convolutional neural network, and recurrent neural network. By exper-iment, we confirm that deep-learning-based algorithms can uniquely distinguish 50 NFC tags with up to 96.16 percent accuracy. We also discuss some of the key technical challenges involved in the use of deep-learning-based RF fingerprinting for NFC.
Gauge invariance and Ward identities in nonlinear response theory

We present a formal analysis of nonlinear response functions in terms of correlation functions in real- and imaginary-time domains. In particular, we show that causal nonlinear response functions, expressed in terms of nested commutators in real time, can be obtained from the analytic continuation of time-ordered response functions, which are more easily amenable to diagrammatic calculation. This generalizes the well-known result of linear response theory. We then use gauge invariance arguments to derive exact relations between second-order response functions in density and current channels. These identities, which are non-perturbative in the strength of inter-particle interactions, allow us to establish exact connections between nonlinear optics calculations done in different electromagnetic gauges.
Engineering fast bias-preserving gates on stabilized cat qubits

Stabilized cat codes can provide a biased noise channel with a set of bias-preserving (BP) gates, which can significantly reduce the resource overhead for fault-tolerant quantum computing. All existing schemes of BP gates, however, require adiabatic quantum evolution, with performance limited by excitation loss and nonadiabatic errors during the adiabatic gates. In this paper, we apply a derivative-based leakage-suppression technique to overcome nonadiabatic errors, so that we can implement fast BP gates on Kerr-cat qubits with improved gate fidelity while maintaining high noise bias. When applied to concatenated quantum error correction, the fast BP gates not only can improve the logical error rate but also can reduce resource overhead, which enables more efficient implementation of fault-tolerant quantum computing.
Zero-shot Single Image Restoration through Controlled Perturbation of Koschmieder’s Model

Real-world image degradation due to light scattering can be described based on the Koschmieder’s model. Training deep models to restore such degraded images is challenging as real-world paired data is scarcely available and synthetic paired data may suffer from domain-shift issues. In this paper, a zero-shot single real-world image restoration model is proposed leveraging a theoretically deduced property of degradation through the Koschmieder’s model. Our zero-shot network estimates the parameters of the Koschmieder’s model, which describes the degradation in the input image, to perform image restoration. We show that a suitable degradation of the input image amounts to a controlled perturbation of the Koschmieder’s model that describes the image’s formation. The optimization of the zero-shot network is achieved by seeking to maintain the relation between its estimates of Koschmieder’s model parameters before and after the controlled perturbation, along with the use of a few no-reference losses. Image dehazing and underwater image restoration are carried out using the proposed zero-shot framework, which in general outperforms the state-of-the-art quantitatively and subjectively on multiple standard real-world image datasets. Additionally, the application of our zero-shot framework for low-light image enhancement is also demonstrated.
Deep speaker conditioning for speech emotion recognition

In this work, we explore the use of speaker conditioning sub-networks for speaker adaptation in a deep neural network (DNN) based speech emotion recognition (SER) system. We use a ResNet architecture trained on log spectrogram features, and augment this architecture with an auxiliary network providing speaker embeddings, which conditions multiple layers of the primary classification network on a single neutral speech sample of the target speaker. The whole system is trained end-to-end using a standard cross-entropy loss for utterance-level SER. Relative to the same architecture without the auxiliary embedding sub-network, we are able to improve by 8.3% on IEMOCAP, and by 5.0% and 30.9% on the 2-class and 5-class SER tasks on FAU-AIBO, respectively.
Cryptanalytic Applications of the Polynomial Method for Solving Multivariate Equation Systems over GF(2)

At SODA 2017 Lokshtanov et al. presented the first worst-case algorithms with exponential speedup over exhaustive search for solving polynomial equation systems of degree d in n variables over finite fields. These algorithms were based on the polynomial method in circuit complexity which is a technique for proving circuit lower bounds that has recently been applied in algorithm design. Subsequent works further improved the asymptotic complexity of polynomial method-based algorithms for solving equations over the field $$\mathbb {F}_2$$ . However, the asymptotic complexity formulas of these algorithms hide significant low-order terms, and hence they outperform exhaustive search only for very large values of n. In this paper, we devise a concretely efficient polynomial method-based algorithm for solving multivariate equation systems over $$\mathbb {F}_2$$ . We analyze our algorithm's performance for solving random equation systems, and bound its complexity by about $$n^2 \cdot 2^{0.815n}$$ bit operations for $$d = 2$$ and $$n^2 \cdot 2^{\left( 1 - 1/2.7d\right) n}$$ for any $$d \ge 2$$ . We apply our algorithm in cryptanalysis of recently proposed instances of the Picnic signature scheme (an alternate third-round candidate in NIST's post-quantum standardization project) that are based on the security of the LowMC block cipher. Consequently, we show that 2 out of 3 new instances do not achieve their claimed security level. As a secondary application, we also improve the best-known preimage attacks on several round-reduced variants of the Keccak hash function. Our algorithm combines various techniques used in previous polynomial method-based algorithms with new optimizations, some of which exploit randomness assumptions about the system of equations. In its cryptanalytic application to Picnic, we demonstrate how to further optimize the algorithm for solving structured equation systems that are constructed from specific cryptosystems.
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images

Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The fine-annotated salient images provide class-agnostic and precise object localization guidance for box-supervised images. The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation. Only using 7991 salient images, the weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO. The source code, pretrained models and datasets are available at https://github.com/hustvl/BoxCaseg.
A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization

Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.
Clusformer: A Transformer based Clustering Approach to Unsupervised Large-scale Face and Visual Landmark Recognition

The research in automatic unsupervised visual clustering has received considerable attention over the last couple years. It aims at explaining distributions of unlabeled visual images by clustering them via a parameterized model of appearance. Graph Convolutional Neural Networks (GCN) have recently been one of the most popular clustering methods. However, it has reached some limitations. Firstly, it is quite sensitive to hard or noisy samples. Secondly, it is hard to investigate with various deep network models due to its computational training time. Finally, it is hard to design an end-to-end training model between the deep feature extraction and GCN clustering modeling. This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism. The proposed method is able to robustly deal with noisy or hard samples. It is also flexible and effective to collaborate with different deep network models with various model sizes in an end-to-end framework. The proposed method is evaluated on two popular large-scale visual databases, i.e. Google Landmark and MS-Celeb1M face database, and outperforms prior unsupervised clustering methods. Code will be available at https://github.com/VinAIResearch/Clusformer
A secure blockchain-oriented data delivery and collection scheme for 5G-enabled IoD environment

−There are innumerable ways the Internet of Drones (IoD) technology can impact our society. With the deployment of an airborne network, the IoD can support real-time low-cost delivery of services ranging from military surveillance to a myriad of civilian applications. Nevertheless, the drones employ insecure wireless communication channels to communicate with other entities in the system, inhibiting its induction in sensitive installations if insecure or inefficient Authenticated Key Agreement (AKA) schemes are employed. The blockchain, an open distributed ledger-based technology, is increasingly being adopted to address the security concern as discussed. Recently, Bera et al. presented an efficient blockchain-enabled AKA scheme for data management among various entities in IoD network. However, their scheme does not support anonymity and untraceability for the drones; also, it does not provide resistance to Ground station server impersonation attack, while the protocol has a few redundancies. Later, we proposed an enhanced blockchain-enabled AKA scheme BOD5-IOD to authenticate drones in the system. The BOD5-IOD, other than supporting a robust access control mechanism between drones and GSS, also ensures safe transactions among all entities in the IoD environment. The formal analysis and performance evaluation endorse that our scheme supports security requirements with computational and communication efficiency of 34.4% and 23.3%, respectively.