Reviewerly/exHarmony huggingface dataset:
Hardware implementation of a robust image cryptosystem using reversible cellular-automata rules and 3-D chaotic systems

In this contribution, a robust image cryptosystem built over a diffusion-confusion architecture is established. The design of a hardware pseudorandom number generator is achieved using two different chaotic systems: Lorenz chaotic system and Rössler chaotic system, exploited to build a single generic architecture. The proposed PRNG is designed using the Xilinx system Generator (XSG) tool and implemented onto an FPGA Zynq ZC 020. It showed high statistical properties as it passes all NIST statistical tests and possesses the best hardware performance. The generated key sequences from the PRNG were used in diffusion processes using XOR operation. Moreover, two extra generated bits were used in the permutation process that precedes the confusion. To further increase the complexity and the randomness of the proposed scheme the confusion process is established over reversible cellular automata. The hardware design of the suggested approach is set forth using XSG tool and implemented on FPGA Zynq ZC 020 working at high-level frequency of 184 MHz and a 23 552 mbps throughput value. Then, the hardware cryptosystem is tested over different plain images. To demonstrate the efficiency of this hardware cryptosystem, security analyses such as key sensitivity analysis and statistical analysis among others were performed to validate the security of proposed hardware-cryptosystem showing an entropy value of 7.9998. A comparative study with some recently suggested cryptosystems is established. Experimental results state that the suggested hardware cryptosystem performs better in terms of security and hardware efficiency such as power consumption, resources utilization and throughput.
Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

In order to improve the effect of modern music education, this paper applies the digital information technology of music resources to the construction of the music teaching system and derives two new types of semi-decision-making process reinforcement learning algorithms based on the Bellman optimality equation base on discrete time. Moreover, this paper uses the comparative research methods to obtain the Q-value learning curve of the incremental value iterative reinforcement learning algorithm based on the semi-Markov decision process and the incremental value iterative reinforcement learning algorithm based on the dichotomy to improve the fusion effect of music teaching resources. Finally, this paper combines the actual needs of modern music education to construct an intelligent music teaching model.
Application Effect of Motion Capture Technology in Basketball Resistance Training and Shooting Hit Rate in Immersive Virtual Reality Environment

With the progress of society, sports have become the mainstream of social development. Strengthening the athletic ability of basketball players can effectively improve their shooting percentage. Firstly, virtual reality (VR) technology and motion capture technology are summarized. Secondly, the resistance training and shooting training of basketball players are analyzed and explained. Finally, the algorithm of motion capture technology is designed to capture and optimize the movements of athletes. In addition, a comprehensive evaluation of the shooting percentage of basketball players is carried out. The results show that the motion capture technology proposed here effectively captures the shooting action of basketball players, and the shooting percentage of players is promoted through resistance training. Among all athletes, the highest shooting percentage improvement is around 14% and the lowest is around 4%. In all groups, athletes of different heights have the largest difference in the improvement of shooting percentage. Therefore, this work plays an important role in improving the shooting rate of basketball players through VR technology. It provides technical support for improving the shooting percentage of basketball players and contributes to the progress of athletes' comprehensive athletic ability.
False data injection attacks on data markets for electric vehicle charging stations

Modern societies use machine learning techniques to support complex decision-making processes (e.g., renewable energy and power demand forecasting in energy systems). Data fuels these techniques, so the quality of the data fed into them determines the accuracy of the results. While the amount of data is increasing with the adoption of internet-of-things, most of it is still private. Availability of data limits the application of machine learning. Scientists and industry pioneers are proposing a model that relies on the economics of data markets, where private data can be traded for a price. Cybersecurity analyses of such markets are lacking. In this context, our study makes two contributions. First, it designs a data market for electric vehicle charging stations, which aims to improve the accuracy of electric vehicle charging demand forecasts. Accurate demand forecasts are essential for sustainable operations of the electric vehicle - charging station - power grid ecosystem, which, in turn, facilitates the electrification and decarbonization of the transportation sector. On the other hand, erroneous demand forecasts caused by malicious cyberattacks impose operational challenges to the ecosystem. Thus, the second contribution of our study is to examine the feasibility of false data injection attacks on the data market for electric vehicle charging stations and to propose a defense mechanism against such attacks. We illustrate our results using data from electric vehicle charging stations in Manhattan, New York. We demonstrate that the data market improves forecasting accuracy of charging stations and reduces the effectiveness of false data injection attacks. The purpose of this work is not only to inform electric vehicle charging stations about the economic benefits of data markets, but to promote cyber awareness among data market pioneers and stakeholders.
Examining Teenagers’ Perceptions of Conversational Agents in Learning Settings

Conversational agents (CAs) have gained increasing presence in learning settings. Researchers have explored perceptions of CAs among children and adults, but rarely among teenagers. Understanding how teenagers conceptualize CAs contributes to designing systems to facilitate productive learning for this population. In this study, we surveyed 73 participants in two age groups (30 participants aged 12-13; 43 participants aged 14-15) about their perceptions of a text-based agent’s competence, trustworthiness, and sociability. We asked participants to describe in writing their ideal CAs that incorporate modalities beyond text, such as sound and gesture. We found that younger participants generally viewed the prototype agent as more competent, trustworthy, and sociable. This group more frequently related the agent designs to curricular contexts, while the older group referenced experiences with virtual assistants to desire broad-domain competence. Findings illuminate considerations of experiences in designing CAs’ interactivity and narratives.
The Application of English Vocabulary Education Informatization under 5G and Cloud Computing Environment

In the 5G and cloud computing environment, autonomous learning based on smart mobile devices has a significant effect on improving students’ vocabulary and vocabulary use. This paper adopts a combination of vocabulary testing and interviews, and by building an autonomous English vocabulary learning platform based on 5G and cloud computing technology, learners can learn and use vocabulary in real natural contexts anytime, anywhere through smart mobile devices. Experimental results show that real-time interaction and collaboration with teachers and other learners during the learning process greatly improve the efficiency and quality of their vocabulary learning.
Compositional features of Pb in agricultural soils and geochemical associations conditioning Pb contents in plants

Soil geochemical data is compositional. Hence the studies targeting the potential of accumulation of toxic elements (TE) in plants have to consider the compositional nature of soil chemical environment. In this study, the combined application of compositional data analysis and geospatial mapping was used to investigate Pb geochemical associations in agricultural soils, revealing the link between these associations and Pb contents in plants, as well as identifying source-specific transfer of Pb from soil to plants. The obtained results showed that soil chemical composition was conditioned by the geological peculiarities of the study area and the potential sources of chemical elements' release. Particularly, k-means clustering and CoDa-biplot allows to identify three distinct subsamples and the application of HCA showed that both Pb soil and plants contents were in the same cluster in all subsamples. However, the geochemical association of elements in subsamples I and III suggested that Pb contents in plants were conditioned by the geochemical behaviors of carbonates whereas in subsample II Pb plant contents were presented in a geochemical association (K, Rb, Pb, and Zn) typical for both fertilizers and the potassium feldspar. The transfer factor (TF) for the comparatively higher values is observed for the subsample linked to K, Rb, Pb, and Zn geochemical association. At the same time, the negative influence of carbonates on the Pb availability in the plants was evidenced. The results of this study can serve as a good example for other investigations targeting the role of soil chemical elements compositional features in elements transfer to plant.
A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification

There may be different types of cancer that cause fatal effects in the human body. In general, cancer is nothing but the unnatural growth of blood cells in different parts of the body and is named accordingly. It may be skin cancer, breast cancer, uterus cancer, intestinal cancer, stomach cancer, etc. However, every type of cancer consists of unwanted blood cells which cause issues in the body starting from the minor to death. Cancer cells have the common features in them, and these common features we have used in our work for the processing. Cancer has a significant death rate; however, it is frequently curable with simple surgery if detected in its early stages. A quick and correct diagnosis may be extremely beneficial to both doctors and patients. In several medical domains, the latest deep-learning-based model's performance is comparable to or even exceeds that of human specialists. We have proposed a novel methodology based on a convolutional neural network that may be used for almost all types of cancer detection. We have collected different datasets of different types of common cancer from different sources and used 90% of the sample data for the training purpose, then we reduced it by 10%, and an 80% image set was used for the validation of the model. After that for testing purposes, we fed a sample dataset and obtain the results. The final output clearly shows that the proposed model outperforms the previous model when we compared our methodology with the existing work.
A2TPNet: Alternate Steered Attention and Trapezoidal Pyramid Fusion Network for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) aims at locating the most eye-catching object in visual input by fusing complementary information of RGB modality and depth modality. Most of the existing RGB-D SOD methods integrate multi-modal features to generate the saliency map indiscriminately, ignoring the ambiguity between different modalities. To better use multi-modal complementary information and alleviate the negative impact of ambiguity among different modalities, this paper proposes a novel Alternate Steered Attention and Trapezoidal Pyramid Fusion Network (A2TPNet) for RGB-D SOD composed of Cross-modal Alternate Fusion Module (CAFM) and Trapezoidal Pyramid Fusion Module (TPFM). CAFM is focused on fusing cross-modal features, taking full consideration of the ambiguity between cross-modal data by an Alternate Steered Attention (ASA), and it reduces the interference of redundant information and non-salient features in the interactive process through a collaboration mechanism containing channel attention and spatial attention. TPFM endows the RGB-D SOD model with more powerful feature expression capabilities by combining multi-scale features to enhance the expressive ability of contextual semantics of the model. Extensive experimental results on five publicly available datasets demonstrate that the proposed model consistently outperforms 17 state-of-the-art methods.
Digital Transformation of Signatures: Suggesting Functional Symmetry Approach for Loan Agreements

This article aims to formulate proposals for regulatory bodies whose implementation would ensure the effective introduction of civil circulation into electronic signatures, with minimal costs for economic entities. While electronic signatures have been widely discussed in academic literature, there are still gaps in the understanding of similarities and differences between electronic and handwritten signatures, the functional specifics of the relationship between them, and the role of electronic signatures for electronic contract. Our research has allowed us to overcome this gap adopting a functional symmetry approach based on measuring the distance between fuzzy sets and the Mamdani fuzzy inference algorithm. This made it possible to form an estimate of the degree of functional symmetry between different types of signatures in a fuzzy and exact form. Correspondingly, we argue that the signature can be viewed as a set of procedures rather than as a single act in order to achieve functional symmetry with a handwritten signature. The case of online lending was used to test and prove this hypothesis. Therefore, regulating electronic signatures needs to focus on the efficiency of this processes for ex ante identification, capturing the intent, ensuring the inalterability and providing reliable evidence, irrespective of the type of electronic signature that is used. It was also revealed that the proposed functional symmetry approach can be combined with a fuzziness index analysis to provide new prospects for further research.
DCGAN-based symmetric encryption end-to-end communication systems

In this article, we propose a symmetric encryption end-to-end communication system based on deep convolutional generative adversarial networks to solve the security problem of transmission in end-to-end learning-based wireless communication systems. The system generates a key by a deep convolutional generative adversarial network and shares it to the transmitter and receiver. Both the transmitter and receiver are represented by convolutional neural networks. We propose to use neural networks as a bridge to make an irreversible mapping relationship between the message and the key. The proposed method achieves more secure message transmission than the symmetric encryption end-to-end communication systems using randomly generated keys. We also propose a concept of the error impact factor to explain the system's characteristics. From the simulation results, the proposed method can achieve encryption transmission of arbitrary length bit messages. The system has excellent performance on additive white Gaussian noise channels and Rayleigh fading channels. The legitimate parties can establish secure transmission in this setting, and the illegal eavesdropper cannot decode accurately without the key.
Enhanced hybrid neural network for automated essay scoring

Abstract In an online learning system, the automatic scoring of an essay is key to providing immediate feedback on essays submitted by students. To the best of our knowledge, existing approaches ignore the multidimensional and heterogeneous characteristics of essays or rely too heavily on the manual creation of features; therefore, a more comprehensive method of scoring essays is required. To address this issue, this paper proposes an enhanced hybrid neural network for automated essay scoring that extracts and fuses the linguistic, semantic, and structural attributes of an essay to achieve a comprehensive representation. Specifically, linguistic attributes include not only lexical features extracted from the words of an essay but also syntactic features obtained from sentences and syntax trees. Semantic attributes include the dynamic textual semantic representation and topic similarity obtained by the text encoder. We also considered the structural attributes. The text encoder provides the overall structural representation, while the sentence similarity matrix provides the two spatial features of connectivity and aggregation. Finally, we fused the three attributes and six features to achieve a more objective and comprehensive automatic scoring. We found that our model improves the Kappa index by an average of 1.4% over the current best model when tested against four state‐of‐the‐art models using eight public data sets.
Assessing and Improving an Evaluation Dataset for Detecting Semantic Code Clones via Deep Learning

In recent years, applying deep learning to detect semantic code clones has received substantial attention from the research community. Accordingly, various evaluation benchmark datasets, with the most popular one as BigCloneBench, are constructed and selected as benchmarks to assess and compare different deep learning models for detecting semantic clones. However, there is no study to investigate whether an evaluation benchmark dataset such as BigCloneBench is properly used to evaluate models for detecting semantic code clones. In this article, we present an experimental study to show that BigCloneBench typically includes semantic clone pairs that use the same identifier names, which however are not used in non-semantic-clone pairs. Subsequently, we propose an undesirable-by-design Linear-Model that considers only which identifiers appear in a code fragment; this model can achieve high effectiveness for detecting semantic clones when evaluated on BigCloneBench, even comparable to state-of-the-art deep learning models recently proposed for detecting semantic clones. To alleviate these issues, we abstract a subset of the identifier names (including type, variable, and method names) in BigCloneBench to result in AbsBigCloneBench and use AbsBigCloneBench to better assess the effectiveness of deep learning models on the task of detecting semantic clones.
The Role of Artificial Intelligence (AI) in Creating Smart Energy Infrastructure for the Next Generation and Protection Climate Change

The application of renewable energy in the world enables in creating of smart energy infrastructure which will help in protecting climate change and prevents the depletion of natural resources. Artificial intelligence in implementing better guidelines for organizing the activities so as to respond to the changing requirements for the next generation. Artificial Intelligence (AI) can enable in overcoming the challenges through adoption of energy-efficient buildings, support in increasing the energy savings, and protect the climate change effectively. The problem related to energy conservation in the building and creation of smart energy infrastructure for the next generation is a multidimensional aspect across countries. It can be stated that AI can enable in offering better solution to the problem through creating smart energy infrastructure which will create comfortable and productive living spaces, it is noted that the complexity of the grids is evolving due to increased energy usage and AI is one of the effective tools which will apply automatic programming which is coupled with the weather conditioning to estimate the full potential of the effective usage of energy and also to take quick action in creating smart energy infrastructure. Through the application of AI, the systems intend to collate and forecast the large volume of information received through the smart grid which is then used to take timely decision making for saving the energy usage and protect climate change. This paper uses secondary data sources in exploring the evolving role of AI in the creation of smart energy infrastructure and provides the necessary steps of implementing such technologies for protecting the climate change in a sustainable manner.
Voice and Gesture based Virtual Desktop Assistant for Physically Challenged People

In the modern era computers are becoming faster, smarter and better. Their usage is rising in the fields such as medicine, business administration, education etc. So there is a need for simplifying the operability and usability of a computer. Digital virtual assistants are known for easing the interaction with computers. Since most of the digital virtual assistants use voice as mode of communication deaf and dumb persons are finding it difficult to use virtual assistants on their devices. This research work attempts to propose a voice and gesture based virtual assistant that can be used by disabled as well as non disabled persons to perform common tasks on their computers. The main aim of this research paper is to develop natural human-machine interaction. Input to the virtual assistant is the user's choice. Users can choose to communicate using voice and gestures or operate a mouse pointer using gestures. In thee proposed system, gestures are recognized even in low light conditions. So, the proposed system is very much helpful in all day conditions. It can be of great help in times like Covid-19 pandemic to be contact free as well as for people with disabilities.
Reducing unitary coupled cluster circuit depth by classical stochastic amplitude prescreening

Unitary coupled cluster (UCC) approaches are an appealing route to utilizing quantum hardware to perform quantum chemistry calculations, as quantum computers can in principle perform UCC calculations in a polynomially scaling fashion, as compared with the exponential scaling required on classical computers. Current noisy intermediate scale quantum computers are limited by both hardware capacity in number of logical qubits and the noise introduced by the deep circuits required for UCC calculations using the variational quantum eigensolver (VQE) approach. We present a combined classical-quantum approach where a stochastic classical UCC preprocessing step is used to determine the important excitations in the UCC Ansatz. The reduced number of selected excitations are then used in a UCC-based VQE calculation. This approach gives a systematically improvable approximation, and we show that significant reductions in quantum resources can be achieved, with simulations on the ${\mathrm{CH}}_{2}, {\mathrm{N}}_{2}$, and ${\mathrm{N}}_{2}{\mathrm{H}}_{2}$ molecules giving submillihartree errors.
Secure Authentication in Wireless Sensor Networks Using Blockchain Technology

A wireless sensor network (WSN) is capable of monitoring, fetching, and transmitting data from one place to another in any condition. The internet of things (IoT) network, a type of WSN, and its protection have always been a big problem. There are lots of risks related to security in this network. Authentication of node identity is an essential security concern. Conventional security algorithms for IoT rely on trustworthy third parties. They might fail at a single stage point. Therefore, in this chapter, the model is proposed as the solution to the security concerns by blockchain concepts. So, in this chapter, a trust model is introduced based on blockchain for WSN for communicating with several nodes without having any risks related to security.