Reviewerly/exHarmony huggingface dataset:
Economic Load Dispatch Problem Based on Search and Rescue Optimization Algorithm

The Search and Rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the exploration's behaviour for humans throughout search and rescue processes.The SAR is applied to solve the Combined Emission and Economic Dispatch (CEED) and Economic Load Dispatch (ELD).The comparative performance of SAR against several metaheuristic methods was performed to assess its reliability.These algorithms include the Earthworm optimization algorithm (EWA), Grey wolf optimizer (GWO), Tunicate Swarm Algorithm (TSA) and Elephant Herding Optimization (EHO) for the same two networks study.Also, the proposed SAR method is compared with other literature algorithms such as Sine Cosine algorithm, Monarch butterfly optimization, Artificial Bee Colony, Chimp Optimization Algorithm, Moth search algorithm.The cases applied in this work are seven cases: three cases of 6-unit for ELD issue, three cases of 6-unit for CEED issue and 10-unit for ELD problem.The evaluation of counterparts is performed for 30 different runs based on measuring the Friedman rank test and robustness curves.Furthermore, the standard deviation, maximum objective function, minimum, mean and values over 30 different runs are applied for a statistical analysis of all used techniques.The obtained results proved the superiority of the SAR in determining the fitness function of ELD and CEED is minimizing the cost of fuel for ELD and emission and fuel costs for CEED.
Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider

Picking up public cloud service providers is now becoming a harder task in an enterprise organization. This paper will help in reducing more hesitation to choose a public cloud service provider. This paper is highlighting computation, storage, and infrastructure is important to service features that have an impact when choosing cloud service providers. Compare these three (AWS, Microsoft Azure, GCP) CSPs concerning service, price, advantages, and highlight significant service features. Studies discuss the primary reason to choose a CSP that normally enhance features, familiarity with the brand, suitable for organization and security parameters considered when choosing CSP. Amazon Web Services proved its leadership by maintaining about 33% share in the market throughout for several quarters irrespective of the market size increased by a factor of 3. Microsoft has shown prominent performance in SaaS. Since 2008, after introducing PaaS in the form of Google App Engine, Google is continuously enhancing its cloud computing services of Google Cloud Platform.
Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments for Blockchain

Nowadays, as lightweight mobile clients become more powerful and widely used, more and more information is stored on lightweight mobile clients, user sensitive data privacy protection has become an urgent concern and prob... | Find, read and cite all the research you need on Tech Science Press
Physical secure key distribution based on chaotic self-carrier phase modulation and time-delayed shift keying of synchronized optical chaos

High speed physical secure key distribution in a classical optical fiber channel is unprecedentedly desired for modern secure communication, but it still remains a worldwide technical challenge. In this paper, we propose and experimentally demonstrate a novel high-speed physical secure key distribution scheme based on chaotic optical signal processing and private hardware modules, which employs chaotic self-carrier phase modulation for chaotic bandwidth expansion and time-delayed shift keying of commonly driven synchronized optical chaos for physical layer security. In this scheme, the entropy source rate of synchronized chaos output from two remote response lasers is greatly expanded by chaotic self-carrier delayed nonlinear phase disturbance, which facilitates high speed key extraction from the entropy source with guaranteed randomness. Moreover, a synchronization recovery time of sub-nanosecond is achieved by dynamic keying of the chaotic delay time after chaos synchronization to accelerate the key distribution rate. Based on the proposed scheme, a high physical key distribution rate of 2.1 Gb/s over 40 km is successfully demonstrated in the experiment. The proposed solution provides a promising strategy for future high-speed key distribution based on chaotic optical signal processing and classical fiber channel.
An Authentication Protocol for Next Generation of Constrained IoT Systems

With the exponential growth of connected Internet of Things (IoT) devices around the world, security protection and privacy preservation have risen to the forefront of design and development of innovative systems and services. For low-value IoT devices that identify and track billion of goods in various industries—such as radio-frequency identification (RFID) tags—this involves multiple challenges in very constrained environments. IoT devices aim to design low-cost, low-complexity infrastructure while enabling robust authentication protocols with reduced latency and energy consumption. Given these challenges, in this article, we present a new lightweight authentication protocol for IoT applications, employing an authenticated-encryption (AE) cryptosystem with associated data (AEAD). Since AEAD algorithms provide data confidentiality and message integrity simultaneously, security analysis [Real-or-Random (RoR) and Scyther] results prove the robustness of the proposed protocol against IoT threats. Furthermore, to measure the computation and communication cost, FPGA and ASIC simulations using four different AEAD candidates of National Institute of Standards and Technology (NIST) lightweight cryptography competition are executed. The implementation results [e.g., 4744 gate equivalent (GE) and 0.87-mw power] clearly show that our novel design can be applied to a wide range of constrained IoT devices complying with low-cost, lightweight, and high-speed requirements.
Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting the variety of the receptive fields. Second, the network has a symmetric structure where the encoder and the decoder paths have similar numbers of channels. We explored two novel revisions: a stacked dilated operation that outputs feature maps from multi-scale receptive fields to replace the consecutive convolutions; an asymmetric architecture with fewer channels in the decoder path. Two novel models were developed: U-Net using the stacked dilated operation (SDU-Net) and asymmetric SDU-Net (ASDU-Net). We used both publicly available and private datasets to assess the efficacy of the proposed models. Extensive experiments confirmed SDU-Net outperformed or achieved performance similar to the state-of-the-art while using fewer parameters (40% of U-Net). ASDU-Net further reduced the model parameters to 20% of U-Net with performance comparable to SDU-Net. In conclusion, the stacked dilated operation and the asymmetric structure are promising for improving the performance of U-Net and its variants.
Cyber Republic

How to make liberal democracies more inclusive and the digital economy more equitable: a guide for the coming Fourth Industrial Revolution. Around the world, liberal democracies are in crisis. Citizens have lost faith in their government; right-wing nationalist movements frame the political debate. At the same time, economic inequality is increasing dramatically; digital technologies have created a new class of super-rich entrepreneurs. Automation threatens to transform the free economy into a zero-sum game in which capital wins and labor loses. But is this digital dystopia inevitable? In Cyber Republic, George Zarkadakis presents an alternative, outlining a plan for using technology to make liberal democracies more inclusive and the digital economy more equitable. Cyber Republic is no less than a guide for the coming Fourth Industrial Revolution and the post-pandemic world. Zarkadakis, an expert on technology and management, explains how artificial intelligence, together with intelligent robotics, sophisticated sensors, communication networks, and big data, will fundamentally reshape the global economy; a new “intelligent machine age” will force us to adopt new forms of economic and political organization. He envisions a future liberal democracy in which intelligent machines facilitate citizen assemblies, helping to extend citizen rights, and blockchains and cryptoeconomics enable new forms of democratic governance and business collaboration. Moreover, the same technologies can be applied to scientific research and technological innovation. We need not fear automation, Zarkadakis argues; in a post-work future, intelligent machines can collaborate with humans to achieve the human goals of inclusivity and equality.
Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection

Undersea/subsea data collection via automated underwater vehicles (AUVs) plays an important role for marine biodiversity research, while it is often much more challenging than the data collection above ground via satellites or AUVs. To enable the automated undersea/subsea data collection system, the AUVs are expected to be able to automatically track the objects of interest through what they can “see” from their mounted underwater cameras, where videos or images could be drastically blurred and degraded in underwater lighting conditions. To solve this challenge, in this work, we propose a cascaded framework by combining a DCGAN (deep convolutional generative adversarial network) with an object detector, i.e., single-shot detector (SSD), named DCGAN+SSD, for the detection of various underwater targets from the mounted camera of an automated underwater vehicle. In our framework, our assumption is that DCGAN can be leveraged to alleviate the impact of underwater conditions and provide the object detector with a better performance for automated AUVs. To optimize the hyperparameters of our models, we applied a particle swarm optimization (PSO)-based strategy to improve the performance of our proposed model. In our experiments, we successfully verified our assumption that the DCGAN+SSD architecture can help improve the object detection toward the undersea conditions and achieve apparently better detection rates over the original SSD detector. Further experiments showed that the PSO-based optimization of our models could further improve the model in object detection toward a more robust and fair performance, making our work a promising solution for tackling the challenges in AUVs.
Communication-Efficient Federated Learning with Adaptive Quantization

Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health. However, high communication overloads for transmitting high-dimensional networks and extra security masks remain a bottleneck of FL. This article proposes a communication-efficient FL framework with an Adaptive Quantized Gradient (AQG), which adaptively adjusts the quantization level based on a local gradient’s update to fully utilize the heterogeneity of local data distribution for reducing unnecessary transmissions. In addition, client dropout issues are taken into account and an Augmented AQG is developed, which could limit the dropout noise with an appropriate amplification mechanism for transmitted gradients. Theoretical analysis and experiment results show that the proposed AQG leads to 18% to 50% of additional transmission reduction as compared with existing popular methods, including Quantized Gradient Descent (QGD) and Lazily Aggregated Quantized (LAQ) gradient-based methods without deteriorating convergence properties. Experiments with heterogenous data distributions corroborate a more significant transmission reduction compared with independent identical data distributions. The proposed AQG is robust to a client dropping rate up to 90% empirically, and the Augmented AQG manages to further improve the FL system’s communication efficiency with the presence of moderate-scale client dropouts commonly seen in practical FL scenarios.
Post-quantum cryptography Algorithm's standardization and performance analysis

-Quantum computer is no longer a hypothetical idea. It is the world's most important technology and there is a race among countries to get supremacy in quantum technology. It is the technology that will reduce the computing time from years to hours or even minutes. The power of quantum computing will be a great support for the scientific community. However, it raises serious threats to cybersecurity. Theoretically, all the cryptography algorithms are vulnerable to attack. The practical quantum computers, when available with millions of qubits capacity, will be able to break nearly all modern public-key cryptographic systems. Before the quantum computers arrive with sufficient ‘qubit’ capacity, we must be ready with quantum-safe cryptographic algorithms, tools, techniques, and deployment strategies to protect the ICT infrastructure. This paper discusses in detail the global effort for the design, development, and standardization of various quantum-safe cryptography algorithms along with the performance analysis of some of the potential quantum-safe algorithms. Most quantum-safe algorithms need more CPU cycles, higher runtime memory, and a large key size. The objective of the paper is to analyze the feasibility of the various quantum-safe cryptography algorithms.
Nighttime Image Dehazing Based on Variational Decomposition Model

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm to constrain the structure component, adopt ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> sparsity term to enforce the piece-wise continuous of the detail layer, and use ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.
Nonstationary data stream classification with online active learning and siamese neural networks✩

We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances’ labels is available. We publicly release our code to the community.
Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval

Abstract Deep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset.
A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 to P × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3 . The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption.
Motivational beliefs, an important contrivance in elevating digital literacy among university students

Students who are intrinsically motivated will frequently perform better than those who are not. This paper examined the relationship between motivational belief strategies and digital literacy in digital learning among university students. Motivational belief is an intrinsic motivation that encourages an individual to self-motivate through specific strategies. This paper has its aims to contribute to assisting students, and academics in utilizing the right motivational strategies to elevate students' digital literacy. Although there is a plethora of studies given students' motivation, studies of motivational belief strategies for elevating digital literacy are still rudimentary. To achieve the research aim, three hypotheses were formulated. A total of 583 respondents were analysed empirically using structural equation modeling-partial least-squares analysis. The findings of the study corroborate a positive and significant relationship between motivational belief strategies and digital literacy competency which signifies the important role of self-motivation in promoting digital literacy as well as preparing students to be a part of the digital future.
Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove

Hand gesture recognition is one of the most widely explored areas under the human-computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.