Reviewerly/exHarmony huggingface dataset:
Anti-Noise 3D Object Detection of Multimodal Feature Attention Fusion Based on PV-RCNN

3D object detection methods based on camera and LiDAR fusion are susceptible to environmental noise. Due to the mismatch of physical characteristics of the two sensors, the feature vectors encoded by the feature layer are in different feature spaces. This leads to the problem of feature information deviation, which has an impact on detection performance. To address this problem, a point-guided feature abstract method is presented to fuse the camera and LiDAR at first. The extracted image features and point cloud features are aggregated to keypoints for enhancing information redundancy. Second, the proposed multimodal feature attention (MFA) mechanism is used to achieve adaptive fusion of point cloud features and image features with information from multiple feature spaces. Finally, a projection-based farthest point sampling (P-FPS) is proposed to downsample the raw point cloud, which can project more keypoints onto the close object and improve the sampling rate of the point-guided image features. The 3D bounding boxes of the object is obtained by the region of interest (ROI) pooling layer and the fully connected layer. The proposed 3D object detection algorithm is evaluated on three different datasets, and the proposed algorithm achieved better detection performance and robustness when the image and point cloud data contain rain noise. The test results on a physical test platform further validate the effectiveness of the algorithm.
Quantum computing and preconditioners for hydrological linear systems

Abstract Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from O ( N ) to $$O(\sqrt{N})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.
3D Gaze Estimation Using RGB-IR Cameras

In this paper, we present a framework for 3D gaze estimation intended to identify the user’s focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time. Once the 3D model is built, the 3D gaze direction is computed starting from the eyeball center and passing through the pupil center to the outside world. This model can also be used to transform the pupil position detected in the IR image into its corresponding position in the RGB image and to detect the gaze direction in the corneal image. This technique circumvents the problem of pupil detection in RGB images, which is especially difficult and unreliable when the scene is reflected in the corneal images. In our approach, the auto-calibration process is transparent and unobtrusive. Users do not have to be instructed to look at specific objects to calibrate the eye tracker. They need only to act and gaze normally. The framework was evaluated in a user study in realistic settings and the results are promising. It achieved a very low 3D gaze error (2.12°) and very high accuracy in acquiring corneal images (intersection over union—IoU = 0.71). The framework may be used in a variety of real-world mobile scenarios (indoors, indoors near windows and outdoors) with high accuracy.
Swing Up and Balance of an Inverted Pendulum Using Reinforced Learning Approach Coupled With a Proportional-Integral-Derivative Controller

Classical approaches in control system theory have been developed for decades, yet further improvement and development of advanced control strategies still represents a challenge, depending on different implementation requirements. An inverted pendulum has been established as a benchmark and in this work it is used for testing new control strategies. Due to improvements of hardware, particularly with respect to improvement of the computational time, as well as due to an increase of data available through acquisition, machine learning and artificial intelligence record success in applications in the field of control. In this work, a control strategy which relies on the techniques from Reinforcement Learning (RL) is used to swing up and balance an inverted pendulum. The technique is implemented in combination with a ProportionalIntegralDerivative (PID) controller used subsequently to keep the pendulum balanced and make the system more robust to disturbances and noise. Laboratory experiment has been carried out to verify the proposed control strategy and it was shown that it is successful in swinging up and balancing the inverted pendulum in real-time even in the presence of disturbances. The implementation shows the effectiveness of combining an RL algorithm with a PID controller in real world applications.
Improved Multi-Verse Optimizer Feature Selection Technique With Application To Phishing, Spam, and Denial Of Service Attacks

Intelligent classification systems proved their merits in different fields including cybersecurity. However, most cybercrime issues are characterized of being dynamic and not static classification problems where the set of discriminative features keep changing with time. This indeed requires revising the cybercrime classification system and pick a group of features that preserve or enhance its performance. Not only this but also the system compactness is regarded as an important factor to judge on the capability of any classification system where cybercrime classification systems are not an exception. The current research proposes an improved feature selection algorithm that is inspired from the well-known multi-verse optimizer (MVO) algorithm. Such an algorithm is then applied to 3 different cybercrime classification problems namely phishing websites, spam, and denial of service attacks. MVO is a population-based approach which stimulates a well-known theory in physics namely multi-verse theory. MVO uses the black and white holes principles for exploration, and wormholes principle for exploitation. A roulette selection schema is used for scientifically modeling the principles of white hole and black hole in exploration phase, which bias to the good solutions, in this case the solutions will be moved toward the best solution and probably to lose the diversity, other solutions may contain important information but didn’t get chance to be improved. Thus, this research will improve the exploration of the MVO by introducing the adaptive neighborhood search operations in updating the MVO solutions. The classification phase has been done using a classifier to evaluate the results and to validate the selected features. Empirical outcomes confirmed that the improved MVO (IMVO) algorithm is capable to enhance the search capability of MVO, and outperform other algorithm involved in comparison.
Improved Whale Optimization Algorithm via the Inertia Weight Method Based on the Cosine Function

&lt;p&gt;Whale Optimization Algorithm (WOA) is a new meta-heuristic algorithm proposed by Australian scholar Mirjalili Seyedali in 2016 based on the feeding behavior of whales in the ocean. In response to the disadvantages of this algorithm, such as low solution accuracy, slow convergence speed and easy to fall into local optimum, an improved Whale Optimization Algorithm (IWOA) is proposed in this paper. We introduce chaotic mapping in the initialization of the algorithm to keep the whale population with diversity; introduce adaptive inertia weights in the spiral position update of humpback whales to prevent the algorithm from falling into local optimum; and introduce Levy flight in the random search for food of humpback whales to improve the global search ability of the algorithm. In the simulation experiments, we compare the algorithm of this paper with other metaheuristic algorithms in seven classical benchmark test functions, and the numerical results of four indexes, minimum, maximum, mean and standard deviation, in different dimensions, illustrate that the algorithm of this paper has better performance results.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Deep probability multi-view feature learning for data clustering

Data in the real world is often represented by multi-view or multi-modality. Single view data of the sample is usually not comprehensive enough, yet multi-view data can favorably describe the characteristics of the samples through complementing to each other. Thus, many researchers pay more attention to the field of multi-view data clustering. Some researchers explore non-negative matrix factorization (NMF) based techniques and subspace learning ideas for multi-view clustering. However, these methods usually have difficulty to tackle noise within data, meanwhile most of them are not robust enough without considering the distribution of the original data. Thus, in this paper, we propose a novel deep probability multi-view feature learning (DPMFL) method to tackle these problems. Specifically, we design a probabilistic matrix factorization (PMF) algorithm, which assumes the data obey Gaussian distribution during noise and dimension reduction, for data preprocessing. Moreover, considering the success of Deep Neural Network (DNN) in the field of machine learning, we integrate DNN with PMF and subspace self-representation for effective consistent and specific multi-view feature learning. A new objective function is thus obtained and the solution processes are presented. Experiments on five popular datasets demonstrate the effectiveness of our proposed method compared with nine state-of-the-art approaches in terms of four evaluation metrics.
Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

In the human body, emotion plays a critical function.Emotion is the most significant subject in human-machine interaction.In economic contexts, emotion detection is equally essential.Emotion detection is crucial in making any decision.Several approaches were explored to determine emotion in text.People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium.There has been some work on emotion detection from the text and sentiment analysis.Although some work has been done in which emotion has been recognized, there are many things to improve.There is not much work to detect racism and analysis sentiment on Ukraine -Russia war.We suggested a unique technique in which emotion is identified, and the sentiment is analyzed.We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war.Our system performs better than prior work.The study increases the accuracy of detecting emotion.To identify emotion and racism, we used classical machine learning and the ensemble method.An unsupervised approach and NLP modules were used to analyze sentiment.The goal of the study is to detect emotion and racism and also analyze the sentiment.
The history of cybernetics and artificial intelligence: a view from Saint Petersburg

In the article the history of cybernetics and artificial intelligence in the world and, particularly in the USSR is outlined starting from the 1940s-1950s. The rapid development of these areas in the 1960s is described in more detail. Special attention is paid to the results of Leningrad (St. Petersburg) researchers, particularly to the work of Vladimir Yakubovich and his scientific school on machine learning, pattern recognition, adaptive systems, intelligent robots and their importance for the further development of cybernetics and artificial intelligence.
Single Image Super-Resolution Reconstruction with Preservation of Structure and Texture Details

In recent years, deep-learning-based single image super-resolution reconstruction has achieved good performance. However, most existing methods pursue a high peak signal-to-noise ratio (PSNR), while ignoring the quality of the structure and texture details, resulting in unsatisfactory performance of the reconstruction results in terms of human subjective perception. To solve this issue, this paper proposes a structure- and texture-preserving image super-resolution reconstruction method. Specifically, two different network branches are used to extract features for image structure and texture details. A dual-coordinate direction perception attention (DCDPA) mechanism is designed to highlight structure and texture features. The attention mechanism fully considers the complementarity and directionality of multi-scale image features and effectively avoids information loss and possible distortion of image structure and texture details during image reconstruction. Additionally, a cross-fusion mechanism is designed to comprehensively utilize structure and texture information for super-resolution image reconstruction, which effectively integrates the structure and texture details extracted by the two branch networks. Extensive experiments verify the effectiveness of the proposed method and its superiority over existing methods.
Multi-condition controlled sedimentary facies modeling based on generative adversarial network

Obtaining reliable sedimentary facies models is critical for geology, which primarily involves inferring conditional distributions from sparse wells. Multiple-point simulation (MPS) captures complex geological patterns by simulating multi-point probabilities, but it is sensitive to the stationarity of training images, and the limited training image is difficult to reflect the uncertainty of the prior geological models. Generative Adversarial Networks (GANs) have recently been proposed to spontaneously simulate multiple geological models that behave differently, generating realizations that respect spatial observations (hard data), with impressive results. In this paper, we explore a more flexible approach to conditioning synthetic realizations beyond hard data, allowing stylistic control over the realizations. We construct a conditional generative adversarial network consisting of a deep generator and a multi-scale discriminator as the conditional mapping model, and obtained realizations with improved visual quality. Based on this, style codes, which are numerical codes representing geological properties, are introduced to extend the existing mapping model. The resulting extended generative model can synthesize images that respect the hard data and exhibit the desired geological style. In addition, we collect and construct a dataset, which contains abundant and realistic fluvial patterns to reflect adequate prior geological knowledge. Extensive experiments have been carried out on this dataset, the results show that the generated images reflect absolute responses to style codes while maintaining the matching rate of 94.78% to the hard data, which can provide new ideas for conditional geological modeling.
Analysis of Caribbean XR Survey Creates an XR Development Strategy as a Path to the Regional Metaverse Evolution

XR provides benefits in innovation, competitiveness and sustainability that offset disruptions in and enhances physical reality. The Caribbean’s metaverse evolution started before the pandemic with the development of XR projects and creatives’ NFTs. The physical isolation during the Covid-19 pandemic accelerated the Caribbean’s interest in the metaverse and XR. In 2020, only 83 participants from Trinidad and Tobago entered the CARIRI AR/VR Challenge to demonstrate their XR ideas. There is a need to encourage and accelerate regional XR development. The purpose of this research is to explore Caribbean XR developers’ experiences to provide an understanding of the factors affecting their XR development. This paper addresses the question: What factors of influence will encourage the development of XR projects in the Caribbean to advance their metaverse development? Online questionnaires issued to Caribbean XR developers from July to December 2021 obtained responses from 77 participants throughout 13 regional countries. The primary data were statistically insignificant and skewed towards two countries (Jamaica and Trinidad &amp; Tobago). Comparative and inferential analyses identified factors of influence, industry sectors, and design foci. The originality of this research is an XR development strategy that incorporates the I4.0, UX, and financial strategies. It establishes the XR project design foci (the user, the purpose and the location). The factors of influence minimum criteria and the industry sector(s) influence each design focus. An initial reference list of industry sectors is education (the preferred option), healthcare, tourism, culture, manufacturing for export, construction, entertainment, game development, agriculture, and environmental protection. The strategy’s value is in enabling content creators to design XR applications to meet consumers’ needs and increase the regional adoption of XR. The impact of the research on the Caribbean is to facilitate a path to the regional metaverse evolution. This research identified the need for a regional XR development policy.
Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.
A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT

The acquisition of massive remote sensing data makes it possible to deeply fuse remote sensing and artificial intelligence (AI). The mobility and cost advantages of new sensing platforms in the Internet of Things (IoT) make them ideal for continuous deployment rather than traditional airborne platforms. However, remote sensing devices are vulnerable to malicious attacks and privacy leaks when sharing data due to the complex architecture and heterogeneity of IoT and the lack of a unified security protection mechanism. Traditional protection methods based on public-key encryption require not only complex operations but also energy consumption, which poses new challenges for resources-limited IoT. The objective of this paper was to propose a lightweight privacy-preserving system for the security of remote-sensing images based on visual cryptography. This stacking-to-see feature of visual cryptography enables the efficient encryption of big data such as high-resolution and multi-scale remote sensing images in resource-constrained IoT. To alleviate image quality degradation in visual cryptography, we combined denoising neural networks to extract high-quality images from encrypted datasets, thus improving the recognition accuracy of loss datasets. We conducted extensive experiments, and the results verify the effectiveness of the proposed method in terms of privacy protection and classification accuracy.
Development of Learning Media for E-Booklet Human Reproductive System Materials to Improve Cognitive Learning Outcomes of High School Students

This study aims to develop a learning media E-Booklet for reproductive system materials to improve students' cognitive learning outcomes in biology subjects for the human reproductive system. This research was conducted from December 2020 to June 2021. The trial in this study consisted of one class using the model one group pretest- posttest design. The results of the validation related to the feasibility of the media E-Booklet obtained a score of 91% in the Very Valid category. The results of the pretest and posttest scores of class XI IPA-1 students in improving students' cognitive learning outcomes. This is indicated by the average value of the pretest before using the learning media, which is 33. While the average posttest after using the learning media is 87 with a Biology minimum completeness criteria value of 72. The N-Gain value has an average of 0.81 or in N Gain% is 81% indicating high category. This shows that the learning media E-Booklet material for the human reproductive system can improve students' cognitive learning outcomes.
Design and Implementation of Garbage Classification Virtual Simulation Game Based on Unity 3D

With the development and progress of the times, the number and types of garbage are increasing, and the corresponding problems of environmental pollution and resource waste are becoming more serious. To alleviate these problems, garbage recycling and reusing become even more important, and garbage cannot be reused without garbage classification. At present, although many cities have begun to carry out garbage classification and have built a lot of garbage classification sites in China, the popularity rate of garbage classification is not very ideal. One of the important reasons is that people lack the concept of garbage classification, so they cannot correctly classify garbage. The most important reason which cause this phenomenon is that the government have not educate the garbage classification in basic education stage in previous years, and now most of the points propaganda way is the monotony of picture and text propaganda, it is extremely boring for contemporary people to accept the single graphic form in the information age, and that forms could not arouse the desire to learning the garbage classification. In order to solve this problem, the paper attempts to design a virtual simulation games, garbage sorting games, that focuses on garbage classification which uses Unity 3D technology. The virtual simulation technology could increase peoples’ interest in learning in the game, while it might make people when playing the garbage sorting games have more fun, and in this way, it could make people acquire more knowledge of garbage classification deeply when they are playing the game.