Reviewerly/exHarmony huggingface dataset:
RETRACTED: Development and application of VR course resources based on embedded system in open education

Virtual Reality VR technology offers a unique approach to enhance the use of complex 3D graphics and visualization environments. The study conducted using virtual reality (VR) technology integrated into the computer-aided design network; VR is a Web-based learning environment. Virtual reality technology is expected to be a traditional 2D computer graphics (CG) CG extended to a 3D real-time simulation program. Virtual reality technology can be used to promote participation and student learning. With VR education, change the delivery of educational content. No user interaction is well suited to see it and is very suitable to create a real or imaginary virtual world. Immersive virtual reality (VR) applications require ultra-high data rate and low latency to run smoothly, designed to improve multiuser wireless VR video (depth of learning support system) VR experiences. It has been proposed streaming: quality and low-latency video block submission. Scenario, VR is that it can describe some fields to be widely applied to education. One of the main features of VR is that it allows for multisensory interaction in the visualization space. This combination of visualization and interactive cosmetic VR of multisensory is, ideally, to see whether suitable for effective learning and try to explain this effect by the benefits of active learning from experience. Also, may want to consider some of the defenders and the application of VR education related to technology-related lottery.
Improved detection performance in blood cell count by an attention-guided deep learning method

Blood cell count plays an important role in the field of clinical medical diagnosis. To effectively automate the counting of blood cells, recently, the deep-learning-based detection method represented by the YOLO has been proposed and used successfully. Nevertheless, the YOLO detection method has difficulties in insufficient positioning of the bounding boxes and in distinguishing overlapping objects. To overcome the limitations, we propose a new deep-learning-based method, termed Attention-YOLO, which is achieved by adding the channel attention mechanism and the spatial attention mechanism to the feature extraction network. By using the filtered and weighted feature vector to replace the original feature vector for residual fusion, Attention-YOLO can help the network to improve the detection accuracy. The experimental results indicate that compared to the standard YOLO network, the Attention-YOLO can achieve a better detection performance in blood cell count without introducing too many additional parameters, where the recognition accuracy of cells (RBCs, WBCs, and platelets) has an improvement of 6.70%, 2.13%, and 10.44%, respectively, and the mean Average Precision (mAP) has an improvement of 7.10%.
The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator

In this study, with an FPGA-board using VHDL, we designed a secure chaos-based stream cipher (SCbSC), and we evaluated its hardware implementation performance in terms of computational complexity and its security. The fundamental element of the system is the proposed secure pseudo-chaotic number generator (SPCNG). The architecture of the proposed SPCNG includes three first-order recursive filters, each containing a discrete chaotic map and a mixing technique using an internal pseudo-random number (PRN). The three discrete chaotic maps, namely, the 3D Chebyshev map (3D Ch), the 1D logistic map (L), and the 1D skew-tent map (S), are weakly coupled by a predefined coupling matrix M. The mixing technique combined with the weak coupling technique of the three chaotic maps allows preserving the system against side-channel attacks (SCAs). The proposed system was implemented on a Xilinx XC7Z020 PYNQ-Z2 FPGA platform. Logic resources, throughput, and cryptanalytic and statistical tests showed a good tradeoff between efficiency and security. Thus, the proposed SCbSC can be used as a secure stream cipher.
Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot’s goal enables the complete coverage of the entire area by reconfiguring to different shapes according to the area’s needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving energy usage during navigation. This implies that the robot is able to cover larger areas entirely with the least required actions. This paper presents a complete path planning (CPP) for hTetran, a polyabolo tiled robot, based on a TSP-based reinforcement learning optimization. This structure simultaneously produces robot shapes and sequential trajectories whilst maximizing the reward of the trained reinforcement learning (RL) model within the predefined polyabolo-based tileset. To this end, a reinforcement learning-based travel sales problem (TSP) with proximal policy optimization (PPO) algorithm was trained using the complementary learning computation of the TSP sequencing. The reconstructive results of the proposed RL-TSP-based CPP for hTetran were compared in terms of energy and time spent with the conventional tiled hypothetical models that incorporate TSP solved through an evolutionary based ant colony optimization (ACO) approach. The CPP demonstrates an ability to generate an ideal Pareto optima trajectory that enhances the robot’s navigation inside the real environment with the least energy and time spent in the company of conventional techniques.
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning

Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.
Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

Magnitude estimation is a vital task within earthquake early warning (EEW) systems (EEWSs). To improve the magnitude determination accuracy after P-wave arrival, we introduce an advanced magnitude prediction model that uses a deep convolutional neural network for earthquake magnitude estimation (DCNN-M). In this paper, we use the inland strong-motion data obtained from the Japan Kyoshin Network (K-NET) to calculate the input parameters of the DCNN-M model. The DCNN-M model uses 12 parameters extracted from 3 s of seismic data recorded after P-wave arrival as the input, four convolutional layers, four pooling layers, four batch normalization layers, three fully connected layers, the Adam optimizer, and an output. Our results show that the standard deviation of the magnitude estimation error of the DCNN-M model is 0.31, which is significantly less than the values of 1.56 and 0.42 for the τ c method and P d method, respectively. In addition, the magnitude prediction error of the DCNN-M model is not affected by variations in the epicentral distance. The DCNN-M model has considerable potential application in EEWSs in Japan.
RETRACTED ARTICLE: An optimised homomorphic CRT-RSA algorithm for secure and efficient communication

Abstract Secure and reliable exchange of information between devices is crucial for any network in the current digital world. This information is maintained on storage devices, routing devices, and communication over the cloud. Cryptographic techniques are used to ensure the secure transmission of data, ensuring the user’s privacy by storing and transmitting data in a particular format. Using encryption, only the intended user possessing the key can access the information. During data or essential transmission, the channel should be secured by using robust encryption techniques. Homomorphic Encryption (HE) techniques have been used in the past for this purpose. However, one of the flaws of the conventional HE is seen either in its slow transmission or fast key decryption. Thus, this paper proposes an optimized Homomorphic Encryption Chinese Remainder Theorem with a Rivest-Shamir-Adleman (HE-CRT-RSA) algorithm to overcome this challenge. The proposed Technique, HE-CRT-RSA, utilizes multiple keys for efficient communication and security. In addition, the performance of the HE-CRT-RSA algorithm was evaluated in comparison with the classical RSA algorithm. The result of the proposed algorithm shows performance improvement with reduced decryption time. It is observed that the proposed HE-CRT-RSA is 3–4% faster than the classical Rivest-Shamir-Adleman (RSA). The result also suggests that HE-CRT-RSA effectively enhances security issues of the cloud and helps to decrease the involvement of intruders or any third party during communication or inside the data/server centers.
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering

We introduce an evaluation methodology for visual question answering (VQA) to better diagnose cases of shortcut learning. These cases happen when a model exploits spurious statistical regularities to produce correct answers but does not actually deploy the desired behavior. There is a need to identify possible shortcuts in a dataset and assess their use before deploying a model in the real world. The research community in VQA has focused exclusively on question-based shortcuts, where a model might, for example, answer "What is the color of the sky" with "blue" by relying mostly on the question-conditional training prior and give little weight to visual evidence. We go a step further and consider multimodal shortcuts that involve both questions and images. We first identify potential shortcuts in the popular VQA v2 training set by mining trivial predictive rules such as co-occurrences of words and visual elements. We then introduce VQA-CounterExamples (VQACE), an evaluation protocol based on our subset of CounterExamples i.e. image-question-answer triplets where our rules lead to incorrect answers. We use this new evaluation in a large-scale study of existing approaches for VQA. We demonstrate that even state-of-the-art models perform poorly and that existing techniques to reduce biases are largely ineffective in this context. Our findings suggest that past work on question-based biases in VQA has only addressed one facet of a complex issue. The code for our method is available at https://github.com/cdancette/detect-shortcuts
ANFIC: Image Compression Using Augmented Normalizing Flows

This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow model, which stacks multiple variational autoencoders (VAE) for greater model expressiveness. The VAE-based image compression has gone mainstream, showing promising compression performance. Our work presents the first attempt to leverage VAE-based compression in a flow-based framework. ANFIC advances further compression efficiency by stacking and extending hierarchically multiple VAE’s. The invertibility of ANF, together with our training strategies, enables ANFIC to support a wide range of quality levels without changing the encoding and decoding networks. Extensive experimental results show that in terms of PSNR-RGB, ANFIC performs comparably to or better than the state-of-the-art learned image compression. Moreover, it performs close to VVC intra coding, from low-rate compression up to perceptually lossless compression. In particular, ANFIC achieves the state-of-the-art performance, when extended with conditional convolution for variable rate compression with a single model. The source code of ANFIC can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dororojames/ANFIC</uri> .
Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

The Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase electronic devices’ accuracy, reliability, and productivity. This paper presents fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images. The MFrLFMs are utilized due to their high accuracy, numerical stability, geometric invariances, and high resistance to various attacks. Based on the most significant features generated from MFrLFMs, after scrambling using a two-dimensional Discrete Henon Map, then XORed with binary scrambled watermark to construct owner share. The proposed watermarking method is implemented using a low-cost Raspberry Pi Linux microprocessor, which ensures the suitability of medical devices in the IoMT environment. We evaluated the robustness of the proposed algorithm against different geometric and common signal processing attacks using various medical images. The proposed method gives better BER, NC, and SSIM values than existing methods.
ℏω versus ℏk: dispersion and energy constraints on time-varying photonic materials and time crystals [Invited]

Photonic time-varying systems have attracted significant attention owing to their rich physics and potential opportunities for new and enhanced functionalities. In this context, the duality of space and time in wave physics has been particularly fruitful to uncover interesting physical effects in the temporal domain, such as reflection/refraction at temporal interfaces and momentum-bandgaps in time crystals. However, the characteristics of the temporal/frequency dimension, particularly its relation to causality and energy conservation ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi class="MJX-variant">ℏ<!-- ℏ --></mml:mi> <mml:mi>ω<!-- ω --></mml:mi> </mml:math> is energy, whereas <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi class="MJX-variant">ℏ<!-- ℏ --></mml:mi> <mml:mi mathvariant="bold-italic">k</mml:mi> </mml:math> is momentum), create challenges and constraints that are unique to time-varying systems and are not present in their spatially varying counterparts. Here, we overview two key physical aspects of time-varying photonics that have only received marginal attention so far, namely temporal dispersion and external power requirements, and explore their implications. We discuss how temporal dispersion, an inherent property of any physical causal material, makes the fields evolve continuously at sharp temporal interfaces and may limit the strength of fast temporal modulations and of various resulting effects. Furthermore, we show that changing the refractive index in time always involves large amounts of energy. We derive power requirements to observe a time-crystal response in one of the most popular material platforms in time-varying photonics, i.e., transparent conducting oxides, and we argue that these effects are almost always obscured by less exotic nonlinear phenomena. These observations and findings shed light on the physics and constraints of time-varying photonics, and may guide the design and implementation of future time-modulated photonic systems.
ANALYZING TEACHERS’ PRAISE IN CLASSROOM INTERACTION

This is qualitative research that applied discourse analysis method. The objectives of this research are to clarify (1) the dominant praise used by the English teachers in the classroom interaction and (2) the function of praise given by the English teachers in the classroom interaction. The informants of this research were two students of English Education Study Program who took Pengenalan Lingkungan Persekolahan Program (PLP).The data were collected by using audio recording and note observation. The results of the research show that (1) the teachers dominantly use okay and good in giving Praise and (2) the functions of the praise are given by both of teachers as: to construct the good communication, to support positive learning behavior, to reward good students conduct and achievement and to express approval, admiration and delight during the teaching-learning process. The result also shows that both of teachers still have limited expression in giving praise.
RETRACTED: Learning spatiotemporal features with 3D DenseNet and attention for gesture recognition

Gesture recognition aims at understanding dynamic gestures of the human body and is one of the most important ways of human–computer interaction; to extract more effective spatiotemporal features in gesture videos for more accurate gesture classification, a novel feature extractor network, spatiotemporal attention 3D DenseNet is proposed in this study. We extend DenseNet with 3D kernels and Refined Temporal Transition Layer based on Temporal Transition Layer, and we also explore attention mechanism in 3D ConvNets. We embed the Refined Temporal Transition Layer and attention mechanism in DenseNet3D, named the proposed network “spatiotemporal attention 3D DenseNet.” Our experiments show that our Refined Temporal Transition Layer performs better than Temporal Transition Layer and the proposed spatiotemporal attention 3D DenseNet in each modality outperforms the current state-of-the-art methods on the ChaLearn LAP Large-Scale Isolated gesture dataset. The code and pretrained model are released in https://github.com/dzf19927/STA3D .
Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.
Formal assessment of some properties of Context-Aware Systems

Context-Aware systems are becoming useful components in autonomic and monitoring applications and the assessment of their properties is an important step towards reliable implementation, especially in safety-criticalapplications. In this paper, using an avalanche/landslide alert system as a running example, we propose a technique, based on Boolean Control Networks, to verify that the system dynamics has stable equilibrium states, corresponding to constant inputs, and hence it does not exhibit oscillatory behaviors, and to establish other useful properties in order to implement a precise and timely alarm system.
Current Trends in Deep Learning Frameworks With Opportunities and Future Prospectus

Artificial Intelligence (AI) is going through its golden era by playing an important role in various real-time applications. Most AI applications are using Machine learning and it represents the most promising path to strong AI. On the other hand, Deep Learning (DL), which is itself a kind of Machine Learning (ML), is becoming more and more popular and successful at different use cases, and is at the peak of developments. Hence, DL is becoming a leader in this domain. To foster the growth of the DL community to a greater extent, many open source frameworks are available which implemented DL algorithms. Each framework is based on an algorithm with specific applications. This chapter provides a brief qualitative review of the most popular and comprehensive DL frameworks, and informs end users of trends in DL Frameworks. This helps them make an informed decision to choose the best DL framework that suits their needs, resources, and applications so they choose a proper career.
Consensus and Reliability: The Case of Two Binary Classifiers

In this paper we consider the problem of estimating the probability of misclas-sification when consensus is achieved between two binary classifiers that are trained on the same training set. Firstly, it is shown that, under consensus, the probability of misclassification compares favourably with that of the best of the two classifiers. Secondly, we provide accurate, and yet simple to compute, estimates of the probability of consensus and the probability of misclassification under consensus. This paper provides a theoretical basis for these estimates and demonstrates their accuracy by simulation results on a synthetic data set and on a medical data set for breast cancer cell classification.
Pengembangan Desain Interaktif 3D VR-Room Patient Menggunakan Unity 3D Engine Dan Evaluasi Usability Testing

The development of Virtual Reality technology is currently being developed even in every field of science, one of which is room mapping. Mapping digital space as a visualization medium has helped provide detailed information through the use of three-dimensional (3D) technology. To build a visualization of the room that is useful for the community, especially in the area of ​​the hospital environment, especially hospital patients so that in fighting the disease patients remain in a state of enthusiasm, safe, comfortable and conducive supported by the environment. With the use of 3D technology, in building an interactive room design VR-Room Patient using the Unity 3D Engine is expected to help patients in choosing treatment rooms for Inpatient Hospital. The planned room description is the Presidential Suite Room, Deluxe Suite, Junior Suite and Class II. The results of this study in the form of visualization illustrated by each area, while the evaluation of the results of this study was conducted by asking respondents to fill in an assessment questionnaire on VR visualization using usability testing using the usability heuristic approach, and the usability test results obtained with an average value of 3.194 taken from the assessment of respondents on a scale of 1 to 4 where this explains that the VR-Room Patient application has a good level of reusability ie the average value is above 3.