Reviewerly/exHarmony huggingface dataset:
MSIAP: A Dynamic Searchable Encryption for Privacy-Protection on Smart Grid With Cloud-Edge-End

With the advent of 5G and the Internet of Things, edge computing and cloud computing with their respective strengths are bound as Cloud-Edge-End Orchestrated (CEEO). The CEEO network integrates artificial intelligence and provides innovative technologies for smart grid applications and services. With massive data transmission on the CEEO network, the trustworthiness of the service node exerts an enormous influence on data privacy. To realize securely share data and decrease the local storage, end-user prefer to encrypt data and upload it to the cloud. Meanwhile, the challenge is how to balance efficiency and security perfectly when users need to find relevant documents containing specific keywords from the CEEO network. In this article, we innovatively propose a searchable encryption scheme that supports multi-keyword subset retrieval, named MSIAP. Specifically, we enhance the Apriori, a data mining algorithm, to mine the relevance of files from massive information and build a multi-level index structure. On this basis, we achieve efficient multi-keyword subset retrieval and dynamic update with insignificant information disclosure in the smart grid. Furthermore, our MSIAP strengthens the present data retrieval methods and enormously reduces the time complexity to accommodate the system of distributed smart grid. Finally, we provide the security analysis and performance evaluations by comparing them with existing works.
Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Face Recognition

Since biometric features are unique to each person and deliver precise identification, they are preferred for applications where security is very important recently. Recognition and verification processes using biometric features are increasing day by day. The biometry of the face has very good for keeping information about the face the quality of low intrusiveness and advanced exactness. There are different biometric techniques as hand geometry, iris recognition, as well as fingerprint recognition, which are proposed for the same purpose. But face recognition is the most efficient and widely-employed biometric technique among them. This biometric method has been at the root of numerous innovations such as Cloud Computing, Social Networks and Big Data analysis over the past decades. An effective face recognition in big dataset is challenging and imperative tasks. In this paper, face recognition is done by using ensemble learning methods which are a part of the proposed intelligent face recognition system. In this paper, we presented different algorithms employing skin color for face detection, histogram tor the feature extraction and ensemble methods for recognition part. Hence, the developed Bagging and Boosting has thoroughly presented their superiority over FERET database. Advanced classification capabilities of the ensemble classifiers are having better accuracy than the existing methods in the face recognition. Experimental results employing a FERET benchmark data set show that the developed ensemble methods have improved the recognition accuracy. The Bagging with Random Forest decision tree has reached 99% correct classification on FERET face database.
Exploring the Possibility of Using Metaverse in Korean Language Education

The concept of EduTech has emerged with recent attempts to apply latest technologies to the educational field. The additional demand for seemingly real-life experiences in the online space has put the spotlight on Metaverse, as it blurs the distinction between the virtual and the real. As of 2020, it is increasingly being used by the MZ generation. The types of technology it uses include augmented reality, mirror worlds, virtual worlds and life logging. Until the present, Metaverse technology catered to content industries such as games; but it can also be valuable in language education to enable interaction between participants in the virtual worlds. It is now a necessity to prepare pre-emptive measures to respond to the global demand for Korean language and improve effectiveness in its education sector. This paper examines instances of the application of Metaverse technology in Korean language education, and presents a class model based on task-based teaching methods. As a result, it is anticipates that this will be used as data to direct Korean language education based on Metaverse.
Study and Development of Self Sanitizing Smart Elevator

The year 2020 will always be remembered as the year of a global pandemic. With many places gradually emerging out of lock-downs, the thought of concerted existence amid COVID-19 is being revisited. One such problem is the use of elevators in any building, use of elevators in the times of COVID can be very risky in the transmission of virus. Based on this hypothesis, this paper presents a methodology for maintaining sanitization and security, by incorporating facial recognition in elevators. These elevators are designed to work using voice commands making them completely contactless, also this system includes a thermal sensor to measure the body temperature of every user and notify everyone around them. Using all these different sensors embedded in machine, the device can do much more than an ordinary elevator and can act as a modern smart device that is capable of solving all the problems single handedly. The key aim of this research paper is to demonstrate how various modules function together to accomplish a common goal: to render the experience of moving between floors smart and sanitary during COVID.
UFOExplorer: Fast and Scalable Sampling-Based Exploration With a Graph-Based Planning Structure

We propose UFOExplorer, a fast and efficient exploration method that scales well with the environment size. An exploration paradigm driven by map updates is proposed to enable the robot to react quicker and to always move towards the optimal exploration goal. For each map update, a dense graph-based planning structure is updated and extended. The planning structure is then used to generate a path using a simple exploration heuristic, which guides the robot towards the closest exploration goal. The proposed method scales well with the environment size, as the planning cost is amortized when updating and extending the planning structure. The simple exploration heuristic performs on par with the most recent state-of-the-art methods in smaller environments and outperforms them in larger environments, both in terms of exploration speed and computational efficiency. The implementation of the method is made available for future research.
Effects of intrinsic decoherence on quantum correlations in a two superconducting charge qubit system

Measurement-induced nonlocality (MIN) quantifies the maximal global effect and bipartite quantum correlation due to the locally invariant projective measurements. In this article, we consider a pair of superconducting charge qubits serving as the physical carrier of quantum information. We study the nonlocal correlation in a pair of superconducting qubits characterized by entanglement and different forms of MIN. Further, we investigate the influence of intrinsic decoherence on the temporal evolution of quantum correlations in the considered physical system. It has shown that the Josephson energy is an effective system parameter against the intrinsic decoherence in enhancing the correlations.
HBlogRec: A Hybridized Cognitive Knowledge Scheme for Blog Recommendation infusing XGBoosting and Semantic Intelligence

In recent times Blogs have become a mass media and the number of blogs is increasing exponentially. As the number of blogs starts to increase, finding the desired blog based on the user's content is necessary so a recommender system is the need of the hour. Although considerable research has been carried out to provide a reliable recommender system, only a few have proposed approaches that are semantically driven; for this type of multiple domain knowledge problem, semantic intelligence and real-world knowledge enrichment is required. This paper proposes a hybrid architecture that integrates cognitive knowledge addition, XGBosting for recommending blogs, based on semantic similarity comparison for which measures like SemantoSim and K-L divergence are used and the results are optimized under the purview of metaheuristics optimization. The proposed HBlogRec records an Accuracy and Precision of 94.27 % and 96.34% respectively. Which is compared and analyzed with benchmark and baseline approaches and is inferred to be superior in terms of performance.
A Design of Power-Efficient AES Algorithm on Artix-7 FPGA for Green Communication

With the development and growth in industries, the society and the environment are facing two huge problems. Advancement in technology have raised the problem of communication and data over safe channels. The power/energy deficiency can be reduced by the practice of Green Communication (GC) technologies and energy efficient comopnents. This paper focuses on the use of these technologies in one framework. In this article a power-efficient Advanced Encryption Standard (AES) algorithm is realized on hardware device. For hardware implementations, Field Programmable Gate Array (FPGA) devices are considered. The AES algorithm is designed on VIVADO tool and the results are analyzed on 28 nanometer (nm) Artix-7 FPGA. The power calculation of the AES algorithm is calculated for different clock speed of the device. And it is detected that the AES algorithm is energy efficient, when the clock speed is 2.0ns for Artix-7 FPGA.
Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.
Filtered Probabilistic Model Predictive Control-Based Reinforcement Learning for Unmanned Surface Vehicles

In this article, we address the difficulty of controlling unmanned surface vehicles (USVs) under unforeseeable and unobservable external disturbances using model-based reinforcement learning (MBRL) without human’s prior knowledge. A novel MBRL approach, filtered probabilistic model predictive control (FPMPC) is proposed to iteratively learn the USV model and an MPC-based policy in a probabilistic way through trial-and-error interactions. Compared with existing MBRL approaches that model the unobservable disturbances as system noise, FPMPC introduces a Bayesian filter process to implicitly translate the system dynamics to a partially-observed Markov decision process to present those disturbances as hidden states. An adaptive sample selection is proposed to remove the redundant learning samples based on the filter belief. Equipped with bias compensation and parallel computation, an FPMPC system, specific for USV, is developed. Evaluated by both position holding and target reaching tasks in a real USV data-driven simulation, FPMPC shows its significant superiority in control performances, generalization capability, and sample efficiency under large disturbances compared with the baseline approaches.
Automatic modulation classification of radar signals utilizing X-net

Automatic modulation classification (AMC) of radar signals has long been a challenge, especially in the area of electronic reconnaissance, where collecting and labeling numerous signal samples are usually harsh and impracticable. In this article, a novel recognition network is proposed for detecting radiation signals under intense noise background with only simulation samples for model training. Owing to the X-shaped structure, the recognition network is named X-net. A residual learning convolutional denoising autoencoder (RLCDAE) and a supplementary classification network based on noise level estimation (NLE) constitute the X-net. Via pre-training of RLCDAE, the robustness against noise is enhanced. Then, a supplementary classification network further improves the recognition performance under low signal-to-noise ratios (SNRs). To evaluate the method, comparative experiments with some excellent algorithms on noise immunity and recognition accuracy are conducted. The cognitive system can still recognize ten kinds of radar signals with an overall precision of 96% even when the SNR is −8 dB. Furthermore, simulation samples trained model is verified by measured data. Outstanding performance proves the effectiveness and superiority of the proposed method on cognitive radar signals under low SNRs.
AI as customer

Purpose The purpose of the paper is to note that customers are not necessarily human and to figure out how best to serve artificial intelligence (AI) customers. The authors also propose several major research streams, as examples, to help launch research on AI customers and how to serve them. Design/methodology/approach The current paper is a conceptual one that draws upon research from many areas to support the ideas proposed. Findings AI customer are proliferating. AI as customers can augment or replace human customers and can be the customer itself. Service providers may also be AI, which means that both humans serving AI customers and AI serving AI customers are relevant here. The authors show that even truly autonomous AI customers are likely to be more common in the future. The authors conclude that reverse engineering will probably not be successful in understanding AI customers and that an approach similar to how we research human consumer behavior is likely to be more useful. Originality/value Virtually, the entire literature on customers and how to serve them assumes that customers are human. With the rapid advancement of AI, purchase decisions are increasingly made by AI, suggesting that it is now important and necessary to consider the possibility of AI customers and how best to serve them. This paper opens the door for such research.
Counterdiabatic control in the impulse regime

Coherent control of complex many-body systems is critical to the development of useful quantum devices. Fast perfect state transfer can be exactly achieved through additional counterdiabatic fields. We show that the additional energetic overhead associated with implementing counterdiabatic driving can be reduced while still maintaining high target state fidelities. This is achieved by implementing control fields only during the impulse regime, as identified by the Kibble-Zurek mechanism. We demonstrate that this strategy successfully suppresses most of the defects that would be generated due to the finite driving time for two paradigmatic settings: the Landau-Zener model and the Ising model. For the latter case, we also investigate the performance of our impulse control scheme when restricted to more experimentally realistic local control fields.
AdaBoost-based transfer learning method for positive and unlabelled learning problem

Positive and unlabelled learning (PU learning) is a problem that the training of a classifier only utilizes labelled positive examples and unlabelled examples. Recently, PU learning has been widely studied and used in a number of areas. In this paper, we present an AdaBoost-based transfer learning method to solve PU Learning problem, which is briefly called AdaTLPU. In the proposed model, by sharing SVM parameters and regularization terms, the source task knowledge is transferred to the target task. At the same time, the similarity of the ambiguous examples towards the positive and negative classes is taken into account to refine the decision boundary of the classifier. Meanwhile, we adopt the AdaBoost method to ensemble the obtained weak classifiers to form a strong classifier for prediction. In addition, we put forward an iterative optimization method to obtain the classifier and present the proof of training error bound for the proposed method. Finally, we organize experiments to explore the performance of AdaTLPU and the results indicate that AdaTLPU can achieve the better performance compared with previous PU learning methods.
Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.
Secure Medical Image Steganography Method Based on Pixels Variance Value and Eight Neighbors

The security aspect of processes and methodologies in the information and communication technology era is the main part. The security of information should a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography, watermark, and cryptography. In cryptography, the secrete message is converted into unintelligible text, but the existence of the secrete message is noticed, on the other hand, watermarking and steganography involve hiding the secrete message in a way that its presence cannot be noticed. Presently, the design and development of an effective image steganography system are facing several challenges such as the low capacity, poor robustness and imperceptibility. To surmount these challenges, a new secure image steganography work called the Pixels Variance (PV) method is proposed along with the eight neighbors method and Huffman coding algorithm to overcome the imperceptibility and capacity issues. In proposed method, a new image partitioning with Henon map is used to increase the security part and has three main stages (preprocessing, embedding, and extracting) each stage has different process. In this method, different standard images were used such as medical images and SIPI-dataset. The experimental result was evaluated with different measurement parameters such as Peak signal-to-noise ratio (PSNR) and Structural Similarity Index (SSIM). In short, the proposed steganography method outperformed the commercially available data hiding schemes, thereby resolved the existing issues.
Research trends in cybercrime victimization during 2010–2020: a bibliometric analysis

Research on cybercrime victimization is relatively diversified; however, no bibliometric study has been found to introduce the panorama of this subject. The current study aims to address this research gap by performing a bibliometric analysis of 387 Social Science Citation Index articles relevant to cybercrime victimization from Web of Science database during the period of 2010-2020. The purpose of the article is to examine the research trend and distribution of publications by five main fields, including time, productive authors, prominent sources, active institutions, and leading countries/regions. Furthermore, this study aims to determine the global collaborations and current gaps in research of cybercrime victimization. Findings indicated the decidedly upward trend of publications in the given period. The USA and its authors and institutions were likely to connect widely and took a crucial position in research of cybercrime victimization. Cyberbullying was identified as the most concerned issue over the years and cyber interpersonal crimes had the large number of research comparing to cyber-dependent crimes. Future research is suggested to concern more about sample of the elder and collect data in different countries which are not only European countries or the USA. Cross-nation research in less popular continents in research map was recommended to be conducted more. This paper contributed an overview of scholarly status of cybercrime victimization through statistical evidence and visual findings; assisted researchers to optimize their own research direction; and supported authors and institutions to build strategies for research collaboration.