Reviewerly/exHarmony huggingface dataset:
A Consortium Blockchain Wallet Scheme Based on Dual-Threshold Key Sharing

In recent years, blockchain has triggered an upsurge in the application of decentralized models and has received more and more attention. For convenience and security considerations, in blockchain applications, users usually use wallets to manage digital assets. The most important data stored in the wallet is the user’s private key, which is also the only identification of the ownership of the encrypted digital assets. Once the private key is lost or stolen, it will bring irreparable losses. We proposed a consortium blockchain wallet scheme based on dual-threshold key protection secret-sharing. By splitting and storing the user’s wallet private key using a secret-sharing method, we can protect our private keys safely and effectively. Our scheme is based on the application scenario of the consortium blockchain. The peers preset by the consortium blockchain store the user’s wallet private key shadow shares, reasonably integrate storage resources, and enhance the solution’s anti-attack ability by setting double thresholds.
Side-by-Side Comparison of Human Perception and Performance Using Augmented, Hybrid, and Virtual Reality

Alternative reality (XR) technologies, including physical, augmented, hybrid, and virtual reality, offer ways for engineered spaces to be evaluated. Traditionally, practitioners (such as those designing spacecraft habitats) have relied on physical mockups to perform such design evaluations, but digital XR technologies present several streamlining advantages over their physical counterparts. These digital environments vary in their level of virtuality, and consequently have different effects on human perception and performance, with respect to a completely physical mockup environment. To date, very little has been done to characterize and quantify such differences in human perception and performance across XR environments of equal fidelity for the same end application. Here, we show that perception and performance in the virtual reality environment most closely mirror those in the physical reality environment, as measured through volumetric assessment and functional task experiments. These experiments required subjects to judge the dimensions of 3D objects and perform operational tasks presented via checklists. Our results highlight the potential for virtual reality systems to accelerate the iterative design of engineered spaces relative to the use of physical mockups, while preserving the human perception and performance characteristics of a completely physical environment. These findings also elucidate specific advantages and disadvantages to specific digital XR technologies with respect to one another and the physical reality baseline. Practitioners may inform their selection of an XR modality for their specific end application based on this comparative analysis, as it contextualizes the niche for each technology in the realm of iterative design for engineered spaces.
A neural decoding algorithm that generates language from visual activity evoked by natural images

Transforming neural activities into language is revolutionary for human-computer interaction as well as functional restoration of aphasia. Present rapid development of artificial intelligence makes it feasible to decode the neural signals of human visual activities. In this paper, a novel Progressive Transfer Language Decoding Model (PT-LDM) is proposed to decode visual fMRI signals into phrases or sentences when natural images are being watched. The PT-LDM consists of an image-encoder, a fMRI encoder and a language-decoder. The results showed that phrases and sentences were successfully generated from visual activities. Similarity analysis showed that three often-used evaluation indexes BLEU, ROUGE and CIDEr reached 0.182, 0.197 and 0.680 averagely between the generated texts and the corresponding annotated texts in the testing set respectively, significantly higher than the baseline. Moreover, we found that higher visual areas usually had better performance than lower visual areas and the contribution curve of visual response patterns in language decoding varied at successively different time points. Our findings demonstrate that the neural representations elicited in visual cortices when scenes are being viewed have already contained semantic information that can be utilized to generate human language. Our study shows potential application of language-based brain-machine interfaces in the future, especially for assisting aphasics in communicating more efficiently with fMRI signals.
Handwritten Text Recognition using Deep Learning (CNN &amp; RNN)

Handwriting Detection is a technique or ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, touch screen, photographs etc. Handwritten Text recognition is one of area pattern recognition.The purpose of pattern recognition is to categorize or classification data or object of one of the classes or categories.Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge.Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task.Research in the handwriting recognition field is focused on deep learning techniques and has achieved breakthrough performance in the last few years.Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation.Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems.This system will be applied to detect the writings of different format.The development of handwriting is more sophisticated, which is found various kinds of handwritten character such as digit, numeral, cursive script, symbols, and scripts including English and other languages.
Generative trapdoors for public key cryptography based on automatic entropy optimization

Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography. Normally, the traditional way in designing a trapdoor is to identify a computationally hard problem, such as the NPC problems. So the trapdoor in a public key encryption mechanism turns out to be a type of limited resource. In this paper, we generalize the methodology of adversarial learning model in artificial intelligence and introduce a novel way to conveniently obtain sub-optimal and computationally hard trapdoors based on the automatic information theoretic search technique. The basic routine is constructing a generative architecture to search and discover a probabilistic reversible generator which can correctly encoding and decoding any input messages. The architecture includes a trapdoor generator built on a variational autoencoder (VAE) responsible for searching the appropriate trapdoors satisfying a maximum of entropy, a random message generator yielding random noise, and a dynamic classifier taking the results of the two generator. The evaluation of our construction shows the architecture satisfying basic indistinguishability of outputs under chosen-plaintext attack model (CPA) and high efficiency in generating cheap trapdoors.
Dual-Path Attention Compensation U-Net for Stroke Lesion Segmentation

For the segmentation task of stroke lesions, using the attention U-Net model based on the self-attention mechanism can suppress irrelevant regions in an input image while highlighting salient features useful for specific tasks. However, when the lesion is small and the lesion contour is blurred, attention U-Net may generate wrong attention coefficient maps, leading to incorrect segmentation results. To cope with this issue, we propose a dual-path attention compensation U-Net (DPAC-UNet) network, which consists of a primary network and auxiliary path network. Both networks are attention U-Net models and identical in structure. The primary path network is the core network that performs accurate lesion segmentation and outputting of the final segmentation result. The auxiliary path network generates auxiliary attention compensation coefficients and sends them to the primary path network to compensate for and correct possible attention coefficient errors. To realize the compensation mechanism of DPAC-UNet, we propose a weighted binary cross-entropy Tversky (WBCE-Tversky) loss to train the primary path network to achieve accurate segmentation and propose another compound loss function called tolerance loss to train the auxiliary path network to generate auxiliary compensation attention coefficient maps with expanded coverage area to perform compensate operations. We conducted segmentation experiments using the 239 MRI scans of the anatomical tracings of lesions after stroke (ATLAS) dataset to evaluate the performance and effectiveness of our method. The experimental results show that the DSC score of the proposed DPAC-UNet network is 6% higher than the single-path attention U-Net. It is also higher than the existing segmentation methods of the related literature. Therefore, our method demonstrates powerful abilities in the application of stroke lesion segmentation.
Improvement of Image Classification by Multiple Optical Scattering

Multiple optical scattering occurs when light propagates in a non-uniform medium. During the multiple scattering, images were distorted and the spatial information they carried became scrambled. However, the image information is not lost but presents in the form of speckle patterns (SPs). In this study, we built up an optical random scattering system based on an liquid crystal display (LCD) and an RGB laser source. We found that the image classification can be improved by the help of random scattering which is considered as a feedforward neural network to extracts features from image. Along with the ridge classification deployed on computer, we achieved excellent classification accuracy higher than 94%, for a variety of data sets covering medical, agricultural, environmental protection and other fields. In addition, the proposed optical scattering system has the advantages of high speed, low power consumption, and miniaturization, which is suitable for deploying in edge computing applications.
Mental Simulation during Literary Reading

Readers experience a number of sensations during reading. They do not - or do not only - process words and sentences in a detached, abstract manner. Instead they “perceive” what they read about. They see descriptions of scenery, feel what characters feel, and hear the sounds in a story. These sensations tend to be grouped under the umbrella terms “mental simulation” and “mental imagery.” This chapter provides an overview of empirical research on the role of mental simulation during literary reading. Our chapter also discusses what mental simulation is and how it relates to mental imagery. Moreover, it explores how mental simulation plays a role in leading models of literary reading and investigates under what circumstances mental simulation occurs during literature reading. Finally, the effect of mental simulation on the literary reader’s experience is discussed, and suggestions and unresolved issues in this field are formulated.
Vector Detection Network: An Application Study on Robots Reading Analog Meters in the Wild

Analog meters equipped with one or multiple pointers are wildly utilized to monitor vital devices’ status in industrial sites for safety concerns. Reading these legacy meters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">autonomously</i> remains an open problem since estimating pointer origin and direction under imaging damping factors imposed in the wild could be challenging. Nevertheless, high accuracy, flexibility, and real-time performance are demanded. In this work, we propose the vector detection network (VDN) to detect analog meters’ pointers given their images, eliminating the barriers for autonomously reading such meters using intelligent agents like robots. We tackled the pointer as a 2-D vector, whose initial point coincides with the tip, and the direction is along tail-to-tip. The network estimates a confidence map, wherein the peak pixels are treated as vectors’ initial points, along with a two-layer scalar map, whose pixel values at each peak form the scalar components in the directions of the coordinate axes. We established the Pointer-10 K dataset composing of real-world analog meter images to evaluate our approach due to no similar dataset is available for now. Experiments on the dataset demonstrated that our methods generalize well to various meters, robust to harsh imaging factors, and run in real-time.
Hybrid PID-Fuzzy controller for autonomous UAV stabilization

In the last two decades, there have been great advances in using Unmanned Aerial Vehicles (UAVs) in several applications. This rapid growth is due to their ability to carry out several types of tasks with high flexibility aligned with reduced risks to human life and cost-effectiveness. The increasing demands for more complex assignments have improved UAVs' capabilities, propelling the rise of platforms with a high degree of autonomy for performing simultaneous tasks with less human intervention. One of the great challenges of maneuvering a UAV for a stabilized flight consists of a highly-coupled nonlinear system with fast dynamics. Thus, attitude control is an essential issue for stabilizing the vehicle or for keeping it in the desired orientation. The PID controller is often used due to its simple structure, good stability properties, and less dependence on the exact system model. However, its tuning process may become complicated in the face of the system nonlinearities. In this scenario, an adjustment error can cause it to lose its flight stability. Therefore, the main contribution of this work is the development of a Fuzzy-PID hybrid controller to control a quadrotor UAV's height stability. The work also considers a classic PID controller for comparison purposes with the proposed hybrid controller. The ROS/Gazebo platform was used to carry out the experiments with both control structures. From the results, it was possible to verify that both PID and Fuzzy-PID controllers could perform the attitude control of the UAV. However, the hybrid control strategy obtained some advantages, such as self-adjustment through system variations.
Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi

With the progress of the times and the rapid development of science and technology, machine learning and artificial intelligence are increasingly used in transportation, logistics, and homes. In terms of pets, pet monitoring has also become very popular in recent years. In this study, a real-time monitoring system for home pets using raspberry pie is developed. The proposed method consists of a raspberry Pi based YOLOv3-Tiny identification system for rapid detection and better boundary frame prediction of the cat behavior. Based on the YOLOv3-Tiny method, the following fine-tuning is implemented: (1) The images collected using the mobile phones are uniformly cropped to 416*416 pixels. (2) The images were captured in different periods and randomly rotated to plus or minus 20 degrees to make the dataset more robust for training. (3) One thousand four hundred pictures of the cat's movements in the room for marking and training are used. The dataset is also used to train the YOLOv3 model. According to the input image categories, the results of the output are categorized into six cat actions. They were sleeping, eating, sitting down, walking, going to the toilet, and search for a trash can. The average accuracy of both models was 98%. Based on environmental constraints like speed and memory consumption, any one of these models can be used for image recognition. The raspberry Pi system continuously captures the images and instantly sends a message to the registered mobile phone to achieve an instant preventive measure if the cat goes to the toilet for too long or flips through the trash can.
Dynamic Projection Mapping for Robust Sphere Posture Tracking Using Uniform/Biased Circumferential Markers

In spatial augmented reality, a widely dynamic projection mapping system has been developed as a novel approach to graphics presentation for widely moving objects in dynamic situations. However, this method necessitates a novel tracking marker design that is resistant to random and complex occlusion and out-of-focus blurring, which conventional markers have not achieved. This article presents a uniform circumferential marker that becomes an ellipse in perspective projection and expresses geometric information. It can track the relative posture of a dynamically moving sphere with high speed, high accuracy, and robustness owing to continuous contour lines, thereby supporting both wide-range movement in the depth direction and human interaction. Moreover, a biased circumferential marker is proposed to embed unique coding, where the absolute posture is decoded with a novel recognition algorithm. Moreover, rough initialization using the geometry of multiple ellipses is proposed for both markers to start the automatic and precise tracking. Real-time rotation visualization onto the surface of a moving sphere is made possible with the high-speed, widely dynamic projection mapping system. The tracking performance is demonstrated to exhibit sufficient basic tracking performance as well as robustness against blurring and occlusion compared to conventional dot-based markers.
The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness Regularizer

Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. When graphs are modeling relations between people, however, they will inevitably reflect biases, prejudices, and other forms of inequity and inequality. An important challenge is thus to design accurate graph modeling approaches while guaranteeing fairness according to the specific notion of fairness that the problem requires. Yet, past work on the topic remains scarce, is limited to debiasing specific graph modeling methods, and often aims to ensure fairness in an indirect manner.
Unsupervised Domain Adaptation Based on Correlation Maximization

This research proposes a novel unsupervised domain adaptation algorithm for cross-domain visual recognition. Distance Correlation-based Domain Adaptation or DCDA algorithm is developed by a correlation measure, called distance correlation. DCDA exploits both the statistical and geometrical properties of the data while embedding the both domain instances to a latent feature space. Unlike many proposed algorithms in the literature that utilize the source domain labels to learn pseudo labels, DCDA further exploits the available information in the source domain labels to discover an appropriate projection operator. The implementation of the proposed DCDA algorithm is easy, and it has a closed-form solution. Our experiments and analyses of the results over a wide variety of benchmark domain adaptation data sets indicate that DCDA has significantly better results in comparison with other state-of-the-art approaches in unsupervised domain adaptation and deep learning literature.
Precision Shooting Training System Using Augmented Reality

Precision shooting training is conventionally done at outdoor shooting ranges. There are also virtual training grounds that use weapons and scenarios that do not provide a completely real experience. When the practitioner begins training, the waste of ammunition represents an economic inconvenience both for him and for the training center. For this reason, this research analyzes the factors and tactical strategies involved in precision shooting practices, developing a virtual augmented reality system that implements three-dimensional objects that facilitate training with short-range weapons. The panorama of vision in the sights of the weapons is expanded, to have a better alignment between the three key points that intervene in the shots. Thus, the training method is modernized, but the reactions caused by the recoil force generated by a shot are preserved, without affecting the practitioner’s cognitive process. When acting within a combat environment, it is necessary to recreate a situational awareness that allows generating a perception, understanding, and anticipation of all the elements of the environment. Experimental tests show a 116.67% increase in effectiveness when compared to the traditional method. Thus, there is a reduction in the consumption of resources, making it a sustainable proposal.
A 2.5 <i>μ</i>W KWS Engine With Pruned LSTM and Embedded MFCC for IoT Applications

Always-on keyword spotting (KWS) hardware is gaining popularity in ultra-low power IoT applications where specific words are used to wake up and activate the power hungry downstream system. This work proposes a low power KWS engine with a power-optimized Mel-frequency cepstral coefficients (MFCC) feature extraction module and a memory-optimized long short term memory (LSTM) accelerator. Our LSTM model is pruned and compressed to reduce 89% of model size and 76% of computation. The LSTM accelerator adopts the weight stationary dataflow to reduce energy. Our simulation using a 40nm CMOS process achieves a power consumption of 2.51 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> , which is at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> lower than the state-of-the-art.
Person Re-Identification Microservice over Artificial Intelligence Internet of Things Edge Computing Gateway

With the increase in the number of surveillance cameras being deployed globally, an important topic is person re-identification (Re-ID), which identifies the same person from multiple different angles and different directions across multiple cameras. However, because of the privacy issues involved in the identification of individuals, Re-ID systems cannot send the image data to cloud, and these data must be processed on edge servers. However, there has been a significant increase in computing resources owing to the processing of artificial intelligence (AI) algorithms through edge computing (EC). Consequently, the traditional AI Internet of Things (AIoT) architecture is no longer sufficient. In this study, we designed a Re-ID system at the AIoT EC gateway, which utilizes a microservice to perform Re-ID calculations on EC and balances efficiency with privacy protection. Experimental results indicate that this architecture can provide sufficient Re-ID computing resources to allow the system to scale up or down flexibly to support different scenarios and demand loads.
Clustering with Scikit-Learn in Python

This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.